Agentic AI Architect

Job Description: Technical skills :

• GenAI & Agentic Frameworks - Semantic Kernel/ LangGraph (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 & ModelOps - Azure AI Foundry (model hosting, versioning, monitoring); Evaluation frameworks (LLM-as-judge, test datasets); Prompt/version control, cost/latency monitoring
• DevOps & Observability - CI/CD pipelines (Azure DevOps / GitHub Actions); Logging, monitoring, observability (App Insights, etc.); Performance tuning and scalability

Responsibilities: 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 & ModelOps

• Define AI safety and guardrails (PII, hallucination control, policy constraints)

• Establish ModelOps and PromptOps 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