Data Architect

Agentic Ai Data ArchitectLocation - Remote (US)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 POCsDesign and govern agentic orchestration framework for multi-step production workflowsEstablish architecture patterns for - RAG and grounding, vector search and retrieval, MCP tool access layer, prompt management and evaluationHave a deep understanding of agentic coding and best practices of using agentic coding for large scale implementationsFamiliarity in implementing A2A or similar frameworks in a large scale environmentPlatform & Integration Design:Define integration architecture across - Lakehouse, ODS, document systems, underwriting systems and third-party APIsDesign configurable, metadata-driven framework for multi-LOB onboardingDefine API/microservices patterns (Python/.NET hybrid)AI & GenAI Enablement:Define where and how to use - GenAI vs deterministic logic, agentic workflows vs pipeline workflowsEstablish multimodal integration approach combining structured, unstructured, and external dataDesign prompt lifecycle, evaluation, and optimization strategyGovernance, Safety & ModelOps:Define AI safety and guardrails (PII, hallucination control, policy constraints)Establish ModelOps and PromptOps frameworksEnsure explainability, auditability, and traceability of AI outputsProgram Leadership:Lead technical execution across AI, data, and platform teamsGuide engineers (AI, data, full-stack) and ensure alignment with architectureDrive technical decisions and stakeholder communicationCandidate Profile:Experience: 10–15+ years in software/data/AI engineering with 4–6+ years in AI/ML/GenAI architectureBackground: Strong experience in designing enterprise-scale platforms and distributed systemsDomain (good to have): Insurance / reinsurance / financial servicesEducation: Bachelor's or Master's in Computer Science, Engineering, Data Science, or related fieldProfile Type: Hands-on architect with ability to balance strategy + executionTechnical skills:GenAI & Agentic Frameworks: Semantic Kernel/LangGraph (or similar orchestration frameworks); LLM integration (Azure OpenAI, OpenAI APIs, etc.); Prompt engineering, prompt lifecycle designRetrieval & RAG: Azure AI Search (indexing, vector search, hybrid search); Embedding pipelines and retrieval optimization; RAG design, grounding strategies, context managementTool Access & Integration: MCP (Model Context Protocol) architecture and tool design; API design (FastAPI / REST / microservices); Integration with enterprise systems and third-party APIsAI Safety & Governance: NVIDIA NeMo Guardrails; Microsoft Presidio (PII detection/masking); Guardrails for prompt injection, hallucination controlEvaluation & ModelOps: Azure AI Foundry (model hosting, versioning, monitoring); Evaluation frameworks (LLM-as-judge, test datasets); Prompt/version control, cost/latency monitoringDevOps & Observability: CI/CD pipelines (Azure DevOps / GitHub Actions); Logging, monitoring, observability (App Insights, etc.); Performance tuning and scalability