Lead Applied AI & Machine Learning Engineer

What you'll do

• Build generative AI, agentic AI, and LLM solutions in Python from proof of concept to production.
• Design context engineering approaches to improve model accuracy, latency, reliability, and user experience.
• Lead enterprise semantic modeling strategy, including ontology standards, governance, and lifecycle management.
• Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers.
• Establish and govern a unified semantic layer for trusted analytics across business intelligence and machine learning.
• Build and maintain pipelines for model training, evaluation, optimization, monitoring, and MLOps.
• Implement responsible AI practices and model risk controls aligned to regulated environments.
• Mentor engineers and data scientists to improve engineering rigor and continuous improvement across the team.

What we're looking for

• Master’s degree in a data science-related discipline and eight years of industry experience, or a PhD in a data science-related discipline.
• Demonstrated experience developing and deploying machine learning and generative AI solutions using Python.
• Proven ability to write and maintain production-quality code, including documentation and maintainable design patterns.
• Experience building automated testing practices, including unit tests, and implementing continuous integration pipelines.
• Experience building and managing data pipelines and processing workflows for analytics and machine learning use cases.
• Strong scientific thinking and structured problem-solving skills, including hypothesis-driven analysis and metric definition.
• Strong written and verbal communication skills to explain complex concepts to technical and non-technical stakeholders.
• Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance (preferred).