Lead AI Engineer

At CFA Institute, the Lead AI Engineer serves as a senior individual contributor focused on designing, building, and delivering production-grade AI and machine learning solutions. Working closely with Product, Data, Security, and other partners, you will help bring scalable, secure, and responsible AI capabilities to enterprise initiatives while providing technical guidance to engineers.

This onsite role is based in Charlottesville, VA. The expected compensation range for this metro location is USD 137,000 - 229,000 per yearly.

What you’ll do

• Lead the architecture and delivery of production-grade AI and machine learning capabilities, integrating models, data pipelines, and AI-enabled product features in line with enterprise engineering standards and strategy.
• Act as a technical subject-matter expert in AI engineering, resolving complex design, performance, and reliability challenges.
• Deliver medium-sized AI engineering projects, or larger projects of lower complexity, managing scope, schedule, risk, quality, and budget where assigned.
• Balance multiple, sometimes competing, priorities across projects and stakeholders, escalating the highest complexity or consequence items to the Senior Director.
• Apply engineering best practices, including MLOps, CI/CD for models, testing, observability, and secure development, and contribute recommendations to improve team standards and ways of working.
• Use sound judgment to adapt or work outside established processes when needed to achieve outcomes, and communicate the decisions and rationale to leadership.
• Diagnose and correct the most complex defects in AI applications, data pipelines, and model behavior, including support for incident response and remediation.
• Contribute to the AI engineering roadmap by evaluating emerging technologies and recommending technical approaches to improve solution quality and better meet user needs.
• Partner with Product, Data, Security, Legal, Risk, and business stakeholders to define and document requirements and support ethical, secure, and compliant AI deployment.
• Provide technical guidance, code review, and mentoring to less-experienced engineers, and oversee the quality of work on assigned projects without formal people management responsibility.
• Maintain current knowledge of AI technologies, external regulation, and industry best practices through ongoing learning.

Required qualifications

• Bachelor’s degree in computer science, data science, AI, engineering, or a related field (master’s degree or equivalent experience preferred).
• 7+ years of progressive experience in software, data, or AI engineering, including delivery of production-grade AI or machine learning solutions in an enterprise environment.
• Advanced expertise in AI/ML engineering, application development, and the software development life cycle, including testing, versioning, and deployment.
• Demonstrated experience leading the delivery of medium-sized projects and providing technical guidance to other engineers (formal people management experience not required).
• Demonstrated ability to resolve complex technical problems, balance competing priorities, and exercise sound judgment on escalation.
• Working knowledge of responsible AI, model governance, and security and compliance considerations for AI systems.
• Strong communication and collaboration skills, including the ability to explain technical concepts to technical and non-technical audiences.

Benefits

• Eligibility for an annual incentive bonus
• 12% employer contribution to a 401(k) or pension plan
• Comprehensive medical benefits package
• Health coverage
• Generous time off
• Competitive retirement plans
• Flexible work options
• Wellbeing and development programs

Compensation notes

• Expected salary range (metro locations: New York, NY and Washington, DC): $137,000 - $229,000 per year
• Expected salary range (national locations): $110,000 - $184,000

Application requirement: Your application must clearly demonstrate how you meet the requirements, as CFA Institute cannot make assumptions about education, experience, or location.