Applied AI and Machine Learning Lead

What you'll do

• Lead end-to-end generative AI and agentic AI initiatives from problem framing to production release.
• Design and evaluate foundation-model approaches including model selection, fine-tuning, and safety controls.
• Establish rigorous evaluation practices for quality, robustness, latency, cost, and risk.
• Build and operationalize production-grade machine learning services using monitoring and incident-ready controls.
• Define semantic modeling standards and lifecycle governance to ensure consistency across systems.
• Implement integration patterns for data pipelines and application interfaces to reduce semantic conflicts.
• Define success metrics and manage technical trade-offs with cross-functional leaders to align with business priorities.
• Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching.

What we're looking for

• Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
• 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
• Strong proficiency in Python and modern machine learning frameworks such as PyTorch.
• Demonstrated ability to lead experimentation and evaluation, including defining metrics and running controlled comparisons.
• Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
• Working knowledge of responsible AI practices, model risk concepts, and governance controls for regulated environments.
• Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
• PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI (preferred).