Agentic AI Architect

Job Title

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 & 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

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/ Lang Graph (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 & 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