Role - Agentic AI Data Architect
Location - 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 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
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