Agentic AI Data Architect

Inizio Partners Corp•New York, NY

1d•Remote

About The Position

We are seeking an experienced Agentic AI Data Architect to define the end-to-end architecture for an agentic AI-enabled platform. This role involves leadership across data, AI, orchestration, and integration layers, with hands-on experience in Proofs of Concept (POCs). You will design and govern the agentic orchestration framework for multi-step production workflows and establish architecture patterns for Retrieval-Augmented Generation (RAG), grounding, vector search, retrieval, Model Context Protocol (MCP) tool access, and prompt management/evaluation. A deep understanding of agentic coding and its large-scale implementation best practices is crucial, along with familiarity in implementing A2A or similar frameworks. The role also includes defining integration architecture across various enterprise systems and third-party APIs, designing a configurable, metadata-driven framework for multi-Line of Business (LOB) onboarding, and establishing API/microservices patterns. You will enable AI and Generative AI (GenAI) by defining the optimal use of GenAI versus deterministic logic and agentic workflows versus pipeline workflows. A multimodal integration approach combining structured, unstructured, and external data will be designed, along with a comprehensive prompt lifecycle, evaluation, and optimization strategy. Furthermore, you will define AI safety and guardrails (PII, hallucination control, policy constraints), establish ModelOps and PromptOps frameworks, and ensure the explainability, auditability, and traceability of AI outputs. As a Program Leader, you will drive technical execution across AI, data, and platform teams, guide engineers, ensure architectural alignment, and manage stakeholder communication.

Requirements

10–15+ years in software/data/AI engineering
4–6+ years in AI/ML/GenAI architecture
Strong experience in designing enterprise-scale platforms and distributed systems
Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field
Hands-on architect with ability to balance strategy + execution
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

Nice To Haves

Insurance / reinsurance / financial services domain experience

Responsibilities

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
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)
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
Define AI safety and guardrails (PII, hallucination control, policy constraints)
Establish ModelOps and PromptOps frameworks
Ensure explainability, auditability, and traceability of AI outputs
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