Agentic AI Architect

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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