Agentic AI Data Architect

United States Digital Space LLC; United States; Remote; USD 140,000 - 230,000; Full time

Job summary

United States Digital Space LLC seeks an experienced Agentic AI Data Architect to lead AI, data, and platform initiatives across enterprise-scale systems. You will shape integration across lakehouse, ODS, underwriting and external APIs, while steering GenAI enablement and governance.

This hands-on leadership role requires balancing strategic direction with execution, guiding AI, data, and full-stack engineers, and driving architecture decisions and stakeholder communication.

Qualifications

• 10–15+ years in software/data/AI engineering with 4–6+ years in AI/ML/GenAI architecture
• Experience designing enterprise-scale platforms and distributed systems
• Domain knowledge in insurance/financial services is a plus
• Bachelor's or Master's in CS/Engineering/Data Science or related field
• Hands-on architect capable of balancing strategy and execution

Responsibilities

• Define integration architecture across Lakehouse, ODS, document systems, Underwriting systems and third-party APIs
• Design configurable, metadata-driven onboarding 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 for structured, unstructured 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 and ensure alignment with architecture
• Drive technical decisions and stakeholder communication

Skills

Architectural leadership

Stakeholder communication

Hands-on architect

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

GenAI Frameworks

LangGraph

Azure OpenAI

OpenAI APIs

Prompt engineering

LLM integration

Azure AI Search

MCP architecture

API design (FastAPI/REST)

Guardrails (PII)

ModelOps & PromptOps

Azure DevOps / GitHub Actions

Job description

Role - Agentic AI Data Architect

Location - Remote (US)

Role & Responsibilities Overview

Architecture & Technical Leadership

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