AI Architect

Unissant•Chantilly, VA

1d•Onsite

About The Position

We are seeking an AI Architect (TS/SCI with Polygraph Required) to join our embedded team at the client site in Northern Virginia and will design and implement advanced artificial intelligence solutions for our federal government clients. The ideal candidate will have deep expertise in agentic AI frameworks, enterprise AI architecture, and experience delivering mission-critical AI solutions in classified environments. Qualified applicants will be subject to security clearance validation / security clearance cross-over and must meet minimum qualifications for SCI access to classified information. This position is contingent upon contract award.

Requirements

12+ years of experience in enterprise architecture, software architecture, or AI/ML engineering roles.
3+ years of hands-on experience with generative AI, large language models (LLMs), and AI agent frameworks.
2+ years of demonstrated experience designing and implementing agentic AI systems with autonomous decision-making capabilities, multi-step workflow orchestration, and intelligent task coordination.
5+ years of experience with cloud platforms (AWS, Azure, Google Cloud) and enterprise AI infrastructure.
3+ years of experience working on federal government contracts , preferably with DoD, Intelligence Community, or civilian agencies.
Experience architecting AI solutions in classified environments and secure enclaves (AWS GovCloud, Azure Government, C2S, etc.).
Proven track record of delivering production AI systems at enterprise scale.
Experience with MLOps, model deployment pipelines, and AI system monitoring.
Agentic AI Frameworks: Deep expertise in agent orchestration platforms (LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar).
LLMs & Foundation Models: Hands-on experience with OpenAI GPT models, Anthropic Claude, open-source models (Llama, Mistral), and model fine-tuning.
AI Architecture Patterns: Advanced knowledge of RAG architectures, vector databases (Pinecone, Weaviate, ChromaDB, pgvector), semantic search, and knowledge graph integration.
Cloud & Infrastructure: Expert-level experience with AWS, Azure, or Google Cloud; containerization (Docker, Kubernetes); infrastructure-as-code (Terraform, CloudFormation).
Programming: Proficiency in Python (required); experience with Java, Go, or Rust a plus.
APIs & Integration: Strong skills in REST/GraphQL API design, microservices architecture, event-driven systems, and enterprise integration patterns.
Data Engineering: Experience with data pipelines, ETL processes, SQL/NoSQL databases, and distributed computing frameworks.
Security: Deep understanding of zero-trust architecture, identity and access management (IAM), secrets management, encryption, and secure AI deployment practices.
DevOps/MLOps: CI/CD pipeline design, automated testing, model versioning, experiment tracking (MLflow, Weights & Biases), and production monitoring
Ability to architect complex AI systems that balance autonomy with governance, security, and compliance requirements.
Strong systems thinking and ability to design for scalability, reliability, and resilience
Excellent problem-solving skills with ability to navigate ambiguous requirements and emerging technologies.
Strategic mindset with ability to align AI technical solutions with federal mission objectives.
Deep understanding of AI ethics, bias mitigation, explainability, and responsible AI principles.
Knowledge of federal compliance frameworks (FedRAMP, FISMA, NIST 800-53, NIST AI RMF, CMMC).
Experience with Agile/Scrum methodologies and DevSecOps practices.
Ability to communicate complex technical concepts to diverse audiences including C-level executives, program managers, and technical teams.
Strong documentation skills including architectural decision records, design documents, and technical specifications.
Proven ability to lead cross-functional teams and influence without direct authority
Experience presenting technical solutions to government clients and supporting proposal efforts.
Active TS/SCI w/ Polygraph required.
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Systems, or related technical field required.

Nice To Haves

Master's degree or PhD in AI, Machine Learning, Computer Science, or related field strongly preferred.
Combination of substantial related experience, training, and education may substitute.
AWS Certified Solutions Architect – Professional (preferred)
TOGAF or Zachman Framework certification (preferred)
Security+ or equivalent DoD 8570-compliant certification (preferred)
AI/ML certifications (AWS Machine Learning Specialty, Google Professional ML Engineer, or similar) a plus

Responsibilities

Design and architect enterprise-scale agentic AI systems capable of autonomous decision-making, multi-step task orchestration, and cross-platform coordination.
Develop architectural frameworks for deploying AI agents that operate within secure boundaries while maintaining context preservation and intelligent routing.
Lead the integration of agentic AI capabilities into existing federal enterprise systems, including CRM, knowledge management, workflow automation, and data platforms.
Establish security-by-design principles for AI agent deployment, including agent-specific access policies, real-time monitoring, behavioral anomaly detection, and comprehensive audit trails.
Design and implement AI governance frameworks ensuring ethical AI use, regulatory compliance, and alignment with federal AI standards (NIST AI Risk Management Framework, DoD Responsible AI principles).
Architect solutions for LLM orchestration, retrieval-augmented generation (RAG), vector databases, and semantic search within classified environments.
Develop multi-agent coordination architectures enabling autonomous agents to collaborate, delegate tasks, and manage dependencies across complex federal workflows.
Create scalable infrastructure designs supporting AI model deployment, monitoring, optimization, and lifecycle management.
Lead technical evaluations of emerging AI technologies, frameworks, and platforms (LangChain, LlamaIndex, AutoGen, CrewAI, etc.) for federal application.
Collaborate with data scientists, ML engineers, security teams, and federal stakeholders to translate mission requirements into AI architectural solutions.
Mentor development teams on AI best practices, prompt engineering, agent design patterns, and responsible AI implementation.
Develop technical documentation, architectural diagrams, and white papers communicating complex AI concepts to technical and non-technical audiences.
Support proposal development efforts with AI technical strategies, architectural designs, and cost estimates.