Published 2026-05-04
This role can be based in US or Canada - EST working hours
Role & Responsibilities Overview:
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
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 & Gen AI Enablement
Define where and how to use - Gen AI 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 & Model Ops
Define AI safety and guardrails (PII, hallucination control, policy constraints)
Establish Model Ops and Prompt Ops 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/Gen AI 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 : Gen AI & Agentic Frameworks - Semantic Kernel/ Lang Graph (or similar orchestration frameworks); LLM integration (Azure Open AI, Open AI 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 (Fast API / REST / microservices); Integration with enterprise systems and third-party APIs AI Safety & Governance - NVIDIA Ne Mo Guardrails; Microsoft Presidio (PII detection/masking); Guardrails for prompt injection, hallucination control Evaluation & Model Ops - Azure AI Foundry (model hosting, versioning, monitoring); Evaluation frameworks (LLM-as-judge, test datasets); Prompt/version control, cost/latency monitoring
Dev Ops & Observability - CI/CD pipelines (Azure Dev Ops / Git Hub Actions); Logging, monitoring, observability (App Insights, etc.); Performance tuning and scalability
