Job Summary: We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.Primary Skill Set:
Job Summary: We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.Job Summary: We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.Job Summary: We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval-augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise-scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.
Job Summary:Primary Skill Set:Primary Skill Set:Primary Skill Set:
Primary Skill Set:- Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.
Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.
Generative AI Expertise:- Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.
Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.Agentic AI & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.
Agentic AI & Multi-Agent Architecture:- Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.
Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.
Model Context Protocol (MCP) & Interoperability:- Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.
Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.
Agent Skills & Extensibility:- Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.
Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.
Retrieval-Augmented Generation (RAG) & Knowledge Architecture:- LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.
LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.LLMOps, Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red-teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.
LLMOps, Evaluation & Responsible AI:- Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.
Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.
Machine Learning Mastery:- Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.
Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.
Technical Proficiency:- Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.
Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP-based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost- and latency-efficient system design for enterprise-grade AI.
Architecture Design:Secondary Skill Set:Secondary Skill Set:Secondary Skill Set:
Secondary Skill Set:- Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
Domain Knowledge:- Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.
Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.
Data Engineering:- AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.
AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.AI Governance, Security & Responsible AI: Familiarity with AI governance, safety, and compliance considerations—data privacy, security, bias and fairness, transparency, auditability, and emerging AI regulations—and how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.
AI Governance, Security & Responsible AI:- Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.
Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams, including data scientists, engineers, business stakeholders, and customers. Ability to convey complex technical concepts to non-technical stakeholders.
Communication Skills:Roles & Responsibilities:Roles & Responsibilities:Roles & Responsibilities:
Roles & Responsibilities:- Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.
Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmap—identifying opportunities, evaluating potential use cases, and proposing innovative, agent-driven solutions that align with business goals.
Generative AI Strategy:- Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.
Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.Model Selection: Evaluate and select appropriate models, agent frameworks, RAG strategies, and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability, complexity, safety, cost, latency, and computational resources.
Model Selection:- Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.
Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions, considering components such as data preprocessing, model training, deployment, and monitoring.
Architectural Design:- Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.
Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AI—agent orchestration, MCP-based tool/data integration, shared skills and connectors, memory and state management, guardrails, human oversight, and observability—to enable safe, reliable, and scalable production deployment across teams.
Agentic & Platform Architecture:- Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.
Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions, ensuring the seamless integration of models into production environments.
Solution Implementation:- Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.
Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.Performance Optimization: Continuously optimize the performance of Generative AI models, addressing issues related to speed, accuracy, and resource utilization.
Performance Optimization:- Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
Outcome Review:- Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.
Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements, technical boundaries, and SLAs. Tailor solutions to meet customer needs and address specific challenges.
Customer Collaboration:- Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.Team Collaboration: Collaborate effectively with cross-functional teams, providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
Team Collaboration:- Industry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplace
Industry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplaceIndustry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplaceIndustry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplaceIndustry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscape—new models, agent frameworks, MCP, skills, and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplace
Industry Awareness:Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer clients, in 59 countries, as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core, empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills, expertise, and ideas from our innovation ecosystem. We are deeply committed to being a well-governed, environmentally sustainable organization where diverse talent thrives in an inclusive workplace