Agentic AI Architect

AI Engineer-Machine Learning Ops Engineering

Technical skills:

• GenAI & Agentic Frameworks
  - Semantic Kernel/ Lang Graph (or similar orchestration frameworks); LLM integration (Azure OpenAI, OpenAI APIs, etc.);
  Prompt engineering, prompt lifecycle design
• Retrieval & RAG
  - Azure AI Search (indexing, vector search, hybrid search);
  Embedding pipelines and retrieval optimization; RAG design, grounding strategies, context management
• Tool Access & Integration - MCP (Model Context Protocol) architecture and tool design; API design (FastAPI / REST / microservices);
  Integration with enterprise systems and third-party APIs
• AI Safety & Governance - NVIDIA NeMo Guardrails;
  Microsoft Presidio (PII detection/masking);
  Guardrails for prompt injection, hallucination control
• Evaluation & Model Ops
  - Azure AI Foundry (model hosting, versioning, monitoring);
  Evaluation frameworks (LLM-as-judge, test datasets);
  Prompt/version control, cost/latency monitoring
• Dev Ops & Observability - CI/CD pipelines (Azure Dev Ops / Git Hub Actions);
  Logging, monitoring, observability (App Insights, etc.);
  Performance tuning and scalability

Role & Responsibilities Overview:

• Architecture & Technical Leadership
• Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers with some real hands-on experience doing POCs
• Design and govern agentic orchestration framework for multi-step production workflows
• Establish architecture patterns for - RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation
• Have a deep understanding of Agentic coding and best practices of using Agentic coding for large scale implementations
• Familiarity in implementing A2A or similar frameworks in a large scale environment
• Platform & Integration Design
• Define integration architecture across
  - Lakehouse, ODS, document systems, Underwriting systems and third-party APIs
• Design configurable, metadata-driven framework for multi-LOB onboarding
• Define API/microservices patterns (Python/.NET hybrid)
• AI & GenAI Enablement
• Define where and how to use
  - GenAI vs deterministic logic, agentic workflows vs pipeline workflows
• Establish multimodal integration approach combining structured, unstructured, and external data
• Design prompt lifecycle, evaluation, and optimization strategy
• Governance, Safety & Model Ops
• Define AI safety and guardrails (PII, hallucination control, policy constraints)
• Establish Model Ops and Prompt Ops frameworks
• Ensure explainability, auditability, and traceability of AI outputs
• Program Leadership
• Lead technical execution across AI, data, and platform teams
• Guide engineers (AI, data, full-stack) and ensure alignment with architecture
• Drive technical decisions and stakeholder communication

Qualifications:

• Education:
  
  Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field