AI/ML Program Manager – Consulting

in utilities, facilities, transportation, water, and/or state and local government markets is key.

You will lead client engagements from discovery through delivery, designing tailored AI/ML programs, shaping policies around responsible AI use, and providing hands-on technical insight into machine learning tools, data curation practices, and large language model (LLM) development.

This position is ideal for someone at the intersection of technology strategy, applied AI/ML, public sector consulting, and business growth.

• Drive business development and new client growth — identifying prospective clients, building relationships with agency decision-makers, and expanding services within existing accounts.
• Serve as the primary advisor to clients on AI/ML adoption strategy, governance, and responsible use policies.
• Provide technical expertise during proposal development, including reviewing RFPs, crafting technical approaches, and estimating LOEs.
• Develop consulting methodologies, solution frameworks, and service offerings in AI/ML.
• Build client trust in AI/ML and guide adoption as a practical tool for improving business and mission outcomes.
• Demonstrate AI/ML capabilities through proofs-of-concept and success stories to build client confidence and accelerate adoption.
• Identify needs and opportunities to incorporate AI/ML for optimizing outcomes while supporting predictive modeling and data-driven decision making for clients.
• Lead needs assessments to identify organizational gaps, training requirements, and technical infrastructure needs.
• Design and deliver AI/ML roadmaps, including implementation frameworks, program structures, and success metrics.
• Establish internal and client-facing governance and compliance frameworks to support the safe and scalable deployment of AI solutions.
• Provide expertise in machine learning, and LLMs—guiding clients on model selection, training pipelines, and deployment practices.
• Strategically utilize foundation models, including fine-tuning, evaluation, and deployment.
• Implement and lead projects using retrieval-augmented generation (RAG) systems, embeddings, and vector databases.
• Advise on tools and platforms across the ML lifecycle.
• Lead project planning, define sprints, estimation, prioritization, and retrospectives.
• Mentor and guide junior developers in both technical and professional development.
• Assess the team's current skills baseline and lead training and upskilling initiatives to stay ahead of current and emerging AI/ML trends.
• Conduct regular code and model reviews, establish best practices, and support SDLC.
• Provide updates to the Data Analytics Manager about project progress, team needs, junior analyst development needs, etc.
• Other duties as assigned.

• Experience in utilities, facilities, transportation, water, and/or state/local government markets
• Demonstrated experience working directly with external clients in a consulting or professional services environment
• Hands-on experience developing and deploying custom, scalable AI/ML applications or internal tools for client organizations — this role requires building solutions, not just implementing them.

• Bachelor’s degree in Computer Science, Computer Engineering, Systems Engineering, Data Science, or related field.
• 15+ years of professional experience in client-facing or consulting work.
• Strong experience with and understanding of ML frameworks and tools.
• Proficiency in Python and experience with additional languages (e.g., Java, R, C++).
• Ability to translate complex AI/ML concepts into clear, actionable guidance for non-technical stakeholders.
• Pre-employment drug screening and background check are conditions of employment. Motor vehicle checks may be required based upon position.

• Master’s degree in Computer Science, Computer Engineering, Systems Engineering, Data Science, or related field.
• 8+ years of professional experience in AI/ML.
• Prior experience designing or leading AI/ML training and upskilling programs.
• Expertise in data curation and preparation, including LLM training workflows and dataset development
• Experience with cloud platforms (Azure, AWS, GCP) and modern MLOps pipelines (Docker, Kubernetes, CI/CD).