AI Lead

Guidehouse•Tysons Corner, VA

2d•Hybrid

About The Position

The AI Lead will set the technical direction and lead the delivery of secure, scalable, and responsible artificial intelligence and machine learning solutions in federal and other highly regulated environments. This role will also support modernization initiatives by helping integrate AI capabilities into evolving platforms, applications, and business processes while maintaining continuity with legacy systems. The role combines hands-on architecture expertise, strategic leadership, and cross-functional collaboration to translate mission needs into production-ready capabilities. The AI Lead will guide multidisciplinary teams, establish AI engineering and governance standards, and ensure solutions are reliable, auditable, cost-effective, and aligned with organizational priorities, security requirements, and responsible AI practices.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field.
TEN (10) years of experience with Six (6) or more years of experience in software engineering, AI/ML, cloud architecture, enterprise technology delivery, or related technical roles; a relevant master’s degree may substitute for up to two years of professional experience.
Three (3) or more years leading AI/ML teams, technical workstreams, or large-scale initiatives in complex, regulated, or mission-driven environments.
Demonstrated experience architecting and delivering production AI/ML solutions across the full lifecycle, from discovery and prototyping through deployment, monitoring, and sustainment.
Strong knowledge of generative AI, large language models, retrieval-augmented generation, prompt engineering, model evaluation, vector search, and agentic application patterns.
Experience with AWS cloud-native architectures and services such as Amazon Bedrock, SageMaker AI, Lambda, ECS or Fargate, S3, API Gateway, and event-driven services.
Knowledge of MLOps and LLMOps practices, CI/CD, automated testing, observability, model governance, security controls, and production support.
Experience with cloud-native software design, APIs, distributed systems, data integration, semantic or hybrid search, and scalable enterprise architectures.
Ability to lead multidisciplinary teams, influence stakeholders, manage technical risk, and explain complex AI concepts to executive and non-technical audiences.
Understanding of responsible AI, data privacy, security, accessibility, and governance principles applicable to regulated environments.

Nice To Haves

Master’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field.
Experience designing enterprise RAG, Graph RAG, AI agent, or multi-agent solutions.
Hands-on experience with Amazon Bedrock capabilities such as Knowledge Bases, Guardrails, AgentCore, Agents, Flows, prompt management, or model evaluation.
Experience with frameworks and protocols such as AWS Strands Agents, LangGraph, Haystack AI, PydanticAI, or Model Context Protocol.
Experience with AI evaluation, safety testing, red teaming, tracing, observability, and runtime governance.
Experience with PostgreSQL and pgvector, graph databases, or enterprise vector database platforms.
Familiarity with MLflow, Kubeflow, SageMaker AI, Azure Machine Learning, or comparable model lifecycle platforms.
Experience supporting federal, healthcare, financial services, or other highly regulated environments.
Knowledge of data governance, ontologies, enterprise data architecture, information assurance, and AI risk management frameworks.
Relevant AWS, cloud architecture, security, data, or AI/ML certifications.

Responsibilities

Define the AI strategy, technical roadmap, reference architectures, and engineering standards that connect mission and business priorities to measurable outcomes.
Lead the end-to-end design and delivery of AI/ML and generative AI solutions, including data ingestion, model selection and development, retrieval-augmented generation, agentic workflows, deployment, monitoring, and sustainment.
Architect secure, scalable solutions across cloud and hybrid environments, applying sound software engineering, API, distributed systems, data, and platform design practices.
Establish and oversee MLOps and LLMOps practices for automated testing, evaluation, release management, observability, traceability, reproducibility, and operational resilience.
Evaluate models, tools, and vendors; guide build-versus-buy decisions; and lead proofs of concept that responsibly translate emerging technologies into enterprise capabilities.
Implement responsible AI and model risk management practices, including documentation, validation, explainability, fairness, privacy, human oversight, bias mitigation, change control, and periodic review.
Ensure solutions align with applicable security, accessibility, records management, privacy, and compliance requirements, including FedRAMP, FISMA, NIST frameworks, and agency-specific policies where applicable.
Lead and mentor cross-functional teams of data scientists, ML engineers, data engineers, software engineers, and platform specialists; conduct design reviews and promote technical excellence.
Partner with executives, product owners, program leaders, security, legal, privacy, and operations teams to communicate options, risks, tradeoffs, and recommendations in clear business terms.
Monitor solution quality, model performance, drift, latency, reliability, adoption, and cost; use feedback and metrics to drive continuous improvement.
Support modernization initiatives by identifying opportunities to embed AI into new and legacy applications, aligning solutions with target architectures, and coordinating phased adoption to reduce delivery and operational risk.

Benefits

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Parental Leave
401(k) Retirement Plan
Group Term Life and Travel Assistance
Voluntary Life and AD&D Insurance
Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
Transit and Parking Commuter Benefits
Short-Term & Long-Term Disability
Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
Employee Referral Program
Corporate Sponsored Events & Community Outreach Care.com annual membership
Employee Assistance Program
Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
Discretionary variable incentive bonus