Agentic AI Architect

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