Enterprise AI Architect

Credence•McLean, VA

About The Position

Credence has an immediate need for an Enterprise AI Architect to join our growing AI and Automation practice. In this role, you will design and lead the development of cutting-edge AI solutions that drive real-world impact across federal programs. You will guide cross-functional teams, shape technical strategy, and mentor engineers in a fast-paced, mission-driven environment. The ideal candidate is a hands-on leader with a strong background in AI/ML development, cloud platforms, and enterprise-scale systems design.

Requirements

Bachelor's degree in Computer Science, AI/ML, Engineering, or a related field; Master's preferred
Minimum of 10+ years of experience in software/AI/ML systems, with at least 3+ years in an architect or principal engineer capacity
Demonstrated experience setting technical direction for AI/ML platforms or programs at enterprise scale
Deep understanding of modern AI patterns, including Transformers, agentic systems, Model Context Protocol (MCP), Agent2Agent (A2A), Mastra, LangGraph, and LLM orchestration frameworks
Hands-on fluency in Python and TypeScript sufficient to prototype, review, and credibly direct implementation
Production experience deploying AI workloads in AWS, or GCP — GovCloud experience strongly preferred
Strong grounding in CI/CD, containerization (Docker, Kubernetes), and DevSecOps principles
Working knowledge of federal security and compliance frameworks (RMF, FedRAMP, NIST 800-53)
Experience with Agile delivery and tools (Git, Jira, Confluence)
Must be a U.S. Citizen and eligible to obtain a Secret clearance

Nice To Haves

Active Secret clearance
Experience with multi-model federation (e.g., LiteLLM, Bedrock, Vertex AI)
Background in data architecture, data fabric, data mesh, or lakehouse patterns
Knowledge of Zero Trust Architecture and ATO processes for AI/ML workloads
Prior experience on federal digital modernization programs, ideally in DoD or large civilian agencies
AWS/GCP Solutions Architect, or equivalent certifications

Responsibilities

Define and maintain the enterprise AI/ML reference architecture, including patterns for agentic systems, retrieval-augmented generation, model federation, and human-in-the-loop workflows
Establish architectural standards, ADRs, and interface control documents (ICDs) that govern how programs adopt AI capabilities
Drive AI platform strategy across AWS and GCP — including GovCloud (IL5/IL6) environments — with attention to vendor lock-in, portability, and total cost of ownership
Lead architecture reviews across programs; advise delivery teams on design trade-offs, integration patterns, and production readiness
Partner with Cybersecurity, Data Engineering, and DevSecOps to ensure AI solutions meet federal security, compliance, and accreditation requirements (RMF, FedRAMP, Zero Trust)
Evaluate emerging AI/ML tools, models, and frameworks; make sourcing and standardization recommendations to leadership
Shape the AI capability roadmap in support of capture, proposal, and growth activities across the federal portfolio
Mentor technical leads and senior engineers; cultivate an architecture community of practice