Agentic AI Data Architect

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, 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