Lead Applied AI & Machine Learning Engineer

What you'll do

• Develop 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 practices, and lifecycle management.
• Create scalable ontologies with domain experts to represent business entities, relationships, rules, and constraints.
• Define semantic integration patterns across data pipelines, APIs, and data contracts to resolve conflicts.
• Establish a unified semantic layer to enable trusted analytics across BI, machine learning, and transactional systems.
• Build and maintain pipelines for model training, evaluation, optimization, monitoring, and MLOps.
• Implement responsible AI practices and risk controls aligned with regulated environments.

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 with documentation and maintainable design patterns.
• Experience building automated testing practices and implementing continuous integration pipelines.
• Experience building and managing data pipelines and processing workflows for analytics and machine learning use cases.
• Strong scientific thinking, structured problem-solving skills, and the ability to communicate complex concepts to diverse stakeholders.
• Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance (preferred).
• Experience implementing RAG, tool use, and evaluation strategies for LLM applications; familiarity with responsible AI techniques (preferred).