Assistant Vice President

Job Description: AI Engineer (12-15 Years Experience) Position Title

Senior AI Engineer / Lead AI Engineer

Experience

12-15 Years

Location

Flexible / Hybrid / Onsite

Job Summary

We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.

Key Responsibilities AI & Generative AI Development

• Design and develop enterprise-grade AI and Generative AI applications.
• Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
• Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
• Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
• Fine-tune, evaluate, and optimize foundation models and LLMs.

Large Language Models (LLMs)

• Work with commercial and open-source LLMs including:
  • Claude
  • GPT Series
  • Llama
  • Mistral
  • Gemma
  • Falcon
• Develop prompt engineering strategies and model evaluation frameworks.
• Optimize inference performance, latency, and cost for production deployments.

Natural Language Processing (NLP)

• Build advanced NLP solutions including:
  • Text Classification
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Text Summarization
  • Question Answering Systems
  • Semantic Search
• Develop multilingual AI solutions.

Data Engineering & AI Data Pipelines

• Design scalable data ingestion, transformation, and feature engineering pipelines.
• Build AI-ready data platforms using structured and unstructured data.
• Integrate data from multiple enterprise systems and external sources.
• Ensure data quality, governance, security, and compliance.

MLOps & AI Operations

• Develop CI/CD pipelines for AI and ML workloads.
• Implement model monitoring, observability, retraining, and governance frameworks.
• Automate AI model deployment and lifecycle management.
• Establish best practices for AI reliability, explainability, and responsible AI.

Architecture & Leadership

• Lead AI solution architecture discussions and design reviews.
• Mentor Data Scientists, ML Engineers, and AI Developers.
• Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
• Drive AI innovation initiatives and proof-of-concepts.

Required Technical Skills AI & Machine Learning

• Machine Learning
• Deep Learning
• Neural Networks
• Generative AI
• Agentic AI
• Reinforcement Learning
• Computer Vision (Good to Have)

LLM & GenAI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• AutoGen
• CrewAI
• Haystack

Programming Languages

• Python (Expert)
• SQL
• PySpark
• Java/Scala (Preferred)

NLP Technologies

• Transformers
• Hugging Face
• BERT
• SpaCy
• NLTK

Data Engineering

• Databricks
• Apache Spark
• Delta Lake
• Azure Data Factory
• Snowflake
• Data Warehousing Concepts

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (Preferred)

MLOps & DevOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Git
• Azure DevOps
• GitHub Actions

Vector Databases

• Pinecone
• Weaviate
• FAISS
• ChromaDB
• Azure AI Search

Mandatory Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• 12-15 years of overall IT experience.
• Minimum 5+ years in AI/ML development.
• Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
• Experience delivering AI solutions in production environments.
• Strong expertise in Python, Data Engineering, and Cloud Platforms.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
• Databricks Generative AI Certification
• AWS Certified Machine Learning Specialty
• Google Professional Machine Learning Engineer

Soft Skills

• Strong communication and presentation skills.
• Excellent stakeholder management capabilities.
• Ability to translate business requirements into AI solutions.
• Team leadership and mentoring experience.
• Strategic thinking and problem-solving skills.

Key Deliverables

• Enterprise AI Solutions
• Generative AI Applications
• Agentic AI Frameworks
• RAG-Based Knowledge Systems
• LLM-Powered Business Applications
• AI Governance & MLOps Frameworks
• Production-Ready Machine Learning Solutions

This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.

Job Description: AI Engineer (12-15 Years Experience) Position Title

Senior AI Engineer / Lead AI Engineer

Experience

12-15 Years

Location

Flexible / Hybrid / Onsite

Job Summary

We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.

Key Responsibilities AI & Generative AI Development

• Design and develop enterprise-grade AI and Generative AI applications.
• Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
• Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
• Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
• Fine-tune, evaluate, and optimize foundation models and LLMs.

Large Language Models (LLMs)

• Work with commercial and open-source LLMs including:
  • Claude
  • GPT Series
  • Llama
  • Mistral
  • Gemma
  • Falcon
• Develop prompt engineering strategies and model evaluation frameworks.
• Optimize inference performance, latency, and cost for production deployments.

Natural Language Processing (NLP)

• Build advanced NLP solutions including:
  • Text Classification
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Text Summarization
  • Question Answering Systems
  • Semantic Search
• Develop multilingual AI solutions.

Data Engineering & AI Data Pipelines

• Design scalable data ingestion, transformation, and feature engineering pipelines.
• Build AI-ready data platforms using structured and unstructured data.
• Integrate data from multiple enterprise systems and external sources.
• Ensure data quality, governance, security, and compliance.

MLOps & AI Operations

• Develop CI/CD pipelines for AI and ML workloads.
• Implement model monitoring, observability, retraining, and governance frameworks.
• Automate AI model deployment and lifecycle management.
• Establish best practices for AI reliability, explainability, and responsible AI.

Architecture & Leadership

• Lead AI solution architecture discussions and design reviews.
• Mentor Data Scientists, ML Engineers, and AI Developers.
• Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
• Drive AI innovation initiatives and proof-of-concepts.

Required Technical Skills AI & Machine Learning

• Machine Learning
• Deep Learning
• Neural Networks
• Generative AI
• Agentic AI
• Reinforcement Learning
• Computer Vision (Good to Have)

LLM & GenAI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• AutoGen
• CrewAI
• Haystack

Programming Languages

• Python (Expert)
• SQL
• PySpark
• Java/Scala (Preferred)

NLP Technologies

• Transformers
• Hugging Face
• BERT
• SpaCy
• NLTK

Data Engineering

• Databricks
• Apache Spark
• Delta Lake
• Azure Data Factory
• Snowflake
• Data Warehousing Concepts

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (Preferred)

MLOps & DevOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Git
• Azure DevOps
• GitHub Actions

Vector Databases

• Pinecone
• Weaviate
• FAISS
• ChromaDB
• Azure AI Search

Mandatory Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• 12-15 years of overall IT experience.
• Minimum 5+ years in AI/ML development.
• Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
• Experience delivering AI solutions in production environments.
• Strong expertise in Python, Data Engineering, and Cloud Platforms.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
• Databricks Generative AI Certification
• AWS Certified Machine Learning Specialty
• Google Professional Machine Learning Engineer

Soft Skills

• Strong communication and presentation skills.
• Excellent stakeholder management capabilities.
• Ability to translate business requirements into AI solutions.
• Team leadership and mentoring experience.
• Strategic thinking and problem-solving skills.

Key Deliverables

• Enterprise AI Solutions
• Generative AI Applications
• Agentic AI Frameworks
• RAG-Based Knowledge Systems
• LLM-Powered Business Applications
• AI Governance & MLOps Frameworks
• Production-Ready Machine Learning Solutions

This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.

Job Description: AI Engineer (12-15 Years Experience) Position Title

Senior AI Engineer / Lead AI Engineer

Experience

12-15 Years

Location

Flexible / Hybrid / Onsite

Job Summary

We are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.

Key Responsibilities AI & Generative AI Development

• Design and develop enterprise-grade AI and Generative AI applications.
• Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
• Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
• Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
• Fine-tune, evaluate, and optimize foundation models and LLMs.

Large Language Models (LLMs)

• Work with commercial and open-source LLMs including:
  • Claude
  • GPT Series
  • Llama
  • Mistral
  • Gemma
  • Falcon
• Develop prompt engineering strategies and model evaluation frameworks.
• Optimize inference performance, latency, and cost for production deployments.

Natural Language Processing (NLP)

• Build advanced NLP solutions including:
  • Text Classification
  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Text Summarization
  • Question Answering Systems
  • Semantic Search
• Develop multilingual AI solutions.

Data Engineering & AI Data Pipelines

• Design scalable data ingestion, transformation, and feature engineering pipelines.
• Build AI-ready data platforms using structured and unstructured data.
• Integrate data from multiple enterprise systems and external sources.
• Ensure data quality, governance, security, and compliance.

MLOps & AI Operations

• Develop CI/CD pipelines for AI and ML workloads.
• Implement model monitoring, observability, retraining, and governance frameworks.
• Automate AI model deployment and lifecycle management.
• Establish best practices for AI reliability, explainability, and responsible AI.

Architecture & Leadership

• Lead AI solution architecture discussions and design reviews.
• Mentor Data Scientists, ML Engineers, and AI Developers.
• Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
• Drive AI innovation initiatives and proof-of-concepts.

Required Technical Skills AI & Machine Learning

• Machine Learning
• Deep Learning
• Neural Networks
• Generative AI
• Agentic AI
• Reinforcement Learning
• Computer Vision (Good to Have)

LLM & GenAI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• AutoGen
• CrewAI
• Haystack

Programming Languages

• Python (Expert)
• SQL
• PySpark
• Java/Scala (Preferred)

NLP Technologies

• Transformers
• Hugging Face
• BERT
• SpaCy
• NLTK

Data Engineering

• Databricks
• Apache Spark
• Delta Lake
• Azure Data Factory
• Snowflake
• Data Warehousing Concepts

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (Preferred)

MLOps & DevOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Git
• Azure DevOps
• GitHub Actions

Vector Databases

• Pinecone
• Weaviate
• FAISS
• ChromaDB
• Azure AI Search

Mandatory Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
• 12-15 years of overall IT experience.
• Minimum 5+ years in AI/ML development.
• Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
• Experience delivering AI solutions in production environments.
• Strong expertise in Python, Data Engineering, and Cloud Platforms.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
• Databricks Generative AI Certification
• AWS Certified Machine Learning Specialty
• Google Professional Machine Learning Engineer

Soft Skills

• Strong communication and presentation skills.
• Excellent stakeholder management capabilities.
• Ability to translate business requirements into AI solutions.
• Team leadership and mentoring experience.
• Strategic thinking and problem-solving skills.

Key Deliverables

• Enterprise AI Solutions
• Generative AI Applications
• Agentic AI Frameworks
• RAG-Based Knowledge Systems
• LLM-Powered Business Applications
• AI Governance & MLOps Frameworks
• Production-Ready Machine Learning Solutions

This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.