Senior AI Lead

LMI•Tysons, VA

3d•$145,000 - $220,000

About The Position

LMI is seeking a Senior AI Lead to support Department of Veterans Affairs modernization initiatives involving artificial intelligence, automation, data, and digital workflow transformation. This full-time role provides senior technical leadership for AI-enabled solutions from discovery and feasibility through prototyping, evaluation, deployment readiness, and iterative improvement. The role helps ensure AI capabilities are mission-aligned, technically sound, measurable, secure, supportable, and appropriate for the users and workflows they serve. The Senior AI Lead translates mission needs into practical solution approaches, with an emphasis on generative AI, retrieval-augmented generation, agentic AI, intelligent automation, and AI-enabled decision support. The role guides multidisciplinary teams through model and platform selection, data and integration dependencies, prompt and orchestration patterns, evaluation, human oversight, observability, performance, cost, and transition to sustainable operations. This role is the senior technical point of coordination for AI execution and works closely with software, data, platform/DevSecOps, product/HCD, QA, cybersecurity/privacy, and governance/architecture resources. Strong candidates combine deep AI and cloud fluency with sound engineering judgment, disciplined experimentation, clear communication, and the ability to lead technical teams through ambiguity. LMI is a digital solutions provider focused on accelerating government impact with innovation and speed. It invests in technology and prototypes ahead of need and provides commercial-grade platforms and mission-ready AI to federal agencies. LMI serves the defense, space, healthcare, and energy sectors, and is headquartered in Tysons, Virginia.

Requirements

Bachelor's degree in computer science, software engineering, data science, artificial intelligence, information systems, engineering, or a related technical field; equivalent professional experience may be considered.
10+ years of progressive experience across software engineering, solution architecture, cloud engineering, data/AI systems, technical delivery, or related disciplines, including responsibility for complex enterprise solutions.
3+ years of recent hands-on or technical-lead experience delivering AI/ML, generative AI, LLM, agentic AI, or AI-enabled automation solutions beyond isolated proofs of concept.
Demonstrated experience translating ambiguous mission or business needs into AI solution approaches, technical architectures, evaluation plans, implementation priorities, and measurable outcomes.
Strong working knowledge of LLM solution patterns, including prompting, retrieval-augmented generation, embeddings/vector retrieval, tool/function calling, agents/orchestration, model selection, context management, and evaluation.
Hands-on technical fluency with Python, APIs, JSON, SQL/data access, cloud services, source/version control, and modern engineering practices sufficient to review code, troubleshoot integrations, and guide engineers.
Experience leading AI solutions in Azure, AWS, Google Cloud, or comparable enterprise environments, including integration with data services, identity/access controls, APIs, logging, monitoring, and deployment pipelines.
Experience establishing AI evaluation and quality practices using test datasets, task-based evaluation, groundedness/relevance checks, failure-mode analysis, human review, observability, regression testing, and release criteria.
Strong understanding of responsible AI delivery considerations, including privacy, security, sensitive data, transparency, human oversight, bias/quality, misuse, model limitations, and proportionate controls based on risk.
Demonstrated ability to lead multidisciplinary technical teams, resolve competing technical perspectives, mentor practitioners, and communicate complex AI concepts and tradeoffs to senior non-technical stakeholders.
Ability to successfully complete and maintain all required federal background investigation, suitability, and Public Trust requirements associated with the position, and comply with applicable VA security, privacy, training, records, and data-handling requirements.

Nice To Haves

Advanced degree.
Recommended certification: Azure AI Engineer Associate, Azure Solutions Architect Expert, AWS AI/ML or Solutions Architect credential, Google Professional Machine Learning Engineer, or comparable cloud/AI certification.
12+ years of enterprise technology experience with 5+ years leading AI/ML, advanced analytics, intelligent automation, or emerging-technology delivery in complex or regulated environments.
Prior Department of Veterans Affairs, VHA, other federal health, federal civilian, or similarly complex regulated-enterprise technology experience.
Experience delivering production or pilot GenAI/agentic AI solutions integrated with enterprise data sources, APIs, workflow systems, retrieval services, identity/access controls, and user-facing applications.
Experience with modern AI engineering ecosystems such as Azure AI Foundry/Azure OpenAI, AWS Bedrock/SageMaker, Google Vertex AI, Databricks, LangChain/LangGraph, Semantic Kernel, LlamaIndex, or comparable platforms/frameworks.
Experience with AI observability, model/prompt/version management, automated evaluation, red teaming/adversarial testing, guardrails/content safety, or enterprise AI lifecycle management.
Experience establishing reusable AI standards, reference patterns, evaluation harnesses, or shared components; additional cloud, security, data, architecture, Agile, or program-management credentials are valued.

Responsibilities

Serve as the senior technical lead for AI-enabled solution delivery from problem framing and feasibility through prototype, evaluation, deployment readiness, and iterative improvement.
Translate VA mission and workflow needs into AI solution approaches, technical requirements, evaluation criteria, and delivery roadmaps that account for data, security, privacy, user, and operational constraints.
Lead technical design decisions for generative AI, retrieval-augmented generation, agentic AI, intelligent automation, AI-assisted decision support, and related AI/ML capabilities.
Guide selection of models, AI services, orchestration frameworks, retrieval approaches, prompt patterns, agent tools, APIs, and cloud services based on mission fit, risk, performance, cost, and maintainability.
Define AI evaluation approaches covering task success, groundedness, relevance, error modes, hallucination risk, latency, cost, safety, human review, and release criteria appropriate to the use case.
Partner with data and domain teams to assess authoritative sources, retrieval quality, data readiness, lineage, access constraints, sensitivity, and fitness for AI-enabled use.
Coordinate technical execution across software development, data, platform/DevSecOps, integration, QA, HCD/product, cybersecurity/privacy, and other disciplines so AI components operate as part of an end-to-end solution.
Establish practical human-in-the-loop, feedback, guardrail, fallback, escalation, and monitoring patterns, especially for consequential operational or clinical-adjacent workflows.
Provide senior review of AI prototypes and implementations, identify material design or engineering risks early, and drive remediation before pilot, release, or scale decisions.
Work with platform and DevSecOps resources on deployment, secrets, logging, observability, versioning, performance, reliability, model/configuration management, and operational support for AI services.
Monitor AI solution economics and advise on model choice, caching, routing, token use, infrastructure, and reuse to improve cost efficiency without compromising mission outcomes.
Communicate technical findings, tradeoffs, risks, and recommendations to VA stakeholders and LMI leadership; mentor technical staff and establish reusable AI engineering, evaluation, and documentation practices.