Sr IT Architect

This role leads architecture for cloud-native AI applications leveraging Microsoft Azure, Azure Databricks, LLMOps/MLOps, and enterprise system integrations, ensuring solutions meet standards for security, scalability, governance, and business value.
The architect partners closely with AI engineers, data teams, platform teams, product owners, and business stakeholders to translate complex business problems into referenceable, reusable AI architectures.

Required Qualifications

• 10+ years of IT experience with 4+ years in enterprise architecture or AI architecture roles.
• Strong hands-on experience with Microsoft Azure cloud architecture.
• Deep expertise in AI/ML platforms, especially Azure Databricks.
• Proven experience designing GenAI solutions (RAG, embeddings, LLM fine-tuning).
• Strong understanding of agentic AI concepts (task agents, orchestration, memory, feedback loops).
• Solid background in distributed systems, APIs, and enterprise integration patterns.
• Experience defining reference architectures and technology standards.

Key Responsibilities

• Enterprise AI Architecture & Strategy
• Define and maintain enterprise-wide architecture standards for AI/ML, GenAI, and Agentic AI platforms across Azure and Databricks.
• Own reference architectures for:
• GenAI applications (RAG, fine-tuning, prompt engineering)
• Agentic AI systems (multi-agent orchestration, agent-to-agent communication)
• AI platform services (model serving, vector search, AI gateways).
• Guide long-term AI platform roadmaps aligned to business strategy and cloud architecture principles.
• AI / GenAI / Agentic AI Solution Design
• Architect end-to-end AI solutions including:
  • Data ingestion and feature pipelines
  • Model training, evaluation, and deployment
  • Retrieval-Augmented Generation (RAG) using vector databases
  • Agent frameworks for task orchestration and enterprise workflows.
  • Design multi-LLM strategies (Azure OpenAI, open-source, and commercial LLMs) with abstraction layers and fallback patterns.
  • Define agent registry, agent orchestration, and governance models for enterprise-scale usage.
  • Azure & Databricks Platform Architecture
  • Lead architecture for Azure-native AI stacks
  • Architect Azure Databricks for: ML training and inference, LLM fine-tuning and evaluation, Vector search and embedding pipelines, MLflow-based lifecycle management.
  • Define cost-optimized, secure, and scalable cloud reference patterns.
  • Enterprise Integration & Interoperability
  • Define integration patterns between AI platforms and: ERP, CRM, PLM, HCM systems. APIs, event-driven architectures, and messaging platforms.
  • Architect AI Gateway and API management patterns for GenAI and agent access.
• Governance, Security & Compliance
  • Establish AI/GenAI governance frameworks covering:
    • Data privacy
    • Model risk management
    • Responsible AI principles
    • Auditability and traceability.
    • Ensure architectures integrate with enterprise IAM, RBAC, and SSO.
    • Define guardrails for safe LLM usage, prompt leakage prevention, and data isolation.
• MLOps / LLMOps / AgentOps
  • Define standards and patterns for:
  • CI/CD for AI and GenAI workloads
  • Model versioning, evaluation, and drift monitoring
  • LLMOps and agent lifecycle management.