Lead Artificial Intelligence/Machine Learning Engineer

About the role

This is a hands-on engineering leadership role focused on scaling teams and production-grade AI/ML and LLM systems. You will lead engineers across a central platform team and multiple delivery pods, helping teams move quickly while maintaining strong technical and quality standards. The role combines people leadership, engineering governance, AI architecture review, and day-to-day delivery support. You will help establish consistent approaches to agent quality, production readiness, code reviews, evaluation, and development practices. Working closely with platform leadership and a mix of internal and contractor engineers, you will remove blockers without creating unnecessary layers of approval. This is an opportunity to shape how AI engineering is delivered at scale in a fast-moving, collaborative environment.

Accountabilities

• Lead engineers across the central platform team and delivery pods, effectively coordinating teams that include both internal employees and contractors.
• Establish and maintain engineering standards covering code quality, AI agent quality, review practices, definition of done, and production readiness.
• Partner with platform leadership to establish and scale a consistent paved path covering templates, pipelines, evaluation practices, and other reusable engineering foundations.
• Ensure delivery teams consistently adopt established platform patterns and engineering standards while maintaining appropriate flexibility for individual initiatives.
• Unblock engineering teams on a daily basis, helping delivery pods maintain momentum without becoming an unnecessary gatekeeper.
• Oversee contractor performance and contribute to the contractor-to-employee conversion pipeline in partnership with the relevant sourcing organization.
• Review AI agent architectures, prompts, and evaluation suites to maintain a credible technical quality bar across teams.
• Balance technical leadership and people management in a flat, fast-moving environment, remaining close enough to the work to guide engineers effectively.

Requirements

• 8+ years of professional engineering experience, including at least 3 years managing engineers who have delivered AI, machine learning, or LLM-based systems into production.
• Proven experience managing blended teams consisting of both contractors and employees while maintaining consistent engineering quality and delivery standards.
• Strong enough hands-on AI/ML and LLM expertise to confidently review agent architectures, prompting approaches, evaluation frameworks, and production-readiness decisions.
• Demonstrated ability to lead engineering teams while remaining technically engaged and capable of making sound architectural and quality decisions.
• Comfortable operating as a player-coach in a flat, fast-paced organization, with a collaborative leadership style focused on enabling teams rather than building unnecessary hierarchy.
• Strong communication, prioritization, problem-solving, and people leadership skills, with the ability to remove blockers and keep teams moving toward delivery.
• Experience in healthcare or another regulated industry is a plus, particularly where quality, reliability, and responsible technology practices are important.
• Experience scaling an engineering team from fewer than 10 people to 30+ is a plus.
• Experience with AI platforms and Azure Machine Learning is relevant to the role.

Benefits

• Competitive benefits package including healthcare coverage, basic life insurance, and short- and long-term disability insurance, according to applicable benefit plans.
• Fully remote work within the United States.
• Opportunity to work alongside experienced professionals in a collaborative and open-door environment.
• Exposure to large-scale, globally impactful technology projects and opportunities to expand your expertise.
• Internal learning opportunities through meetups, conferences, workshops, Udemy access, and language courses.
• Company-paid professional certifications to support continued technical and career development.
• Opportunities for internal mobility across different domains and technology areas.
• Hands-on exposure to cutting-edge AI, machine learning, and digital transformation initiatives.
• Opportunity to lead and influence AI engineering practices across multidisciplinary teams and delivery environments.