Agentic AI Architect

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 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 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 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 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: 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 #J-18808-Ljbffr Inizio Partners Corp