Agentic AI Architect

Role & Responsibilities Overview: 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) Technical Development, Execution Perform hands on development and 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 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 Architecture & Technical Leadership Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers Design and govern agentic orchestration framework for multi-step workflows Establish architecture patterns for - RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation 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 Candidate Profile: Experience : 10–15+ years in software/data/AI engineering with 4–6+ years in AI/ML/GenAI architecture Background : Strong experience in designing enterprise-scale platforms and distributed systems Domain (good to have) : Insurance / reinsurance / financial services Education : Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field Profile Type : Hands-on architect with ability to balance strategy + execution 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 Originally posted on Himalayas