Lead Applied AI & Machine Learning Engineer

Build what's next in enterprise AI-solutions that materially improve how teams make decisions, automate work, and serve internal customers.

You will take generative AI from concept to production, help set the standard for semantic consistency across systems, and partner closely with stakeholders to turn complex business needs into measurable outcomes.

You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions.

As an Applied AI and Machine Learning Lead in the Corporate Technology Data Science and AI team, you will Build what's next in enterprise AI-solutions that materially improve how teams make decisions, automate work, and serve internal customers.

In this role, you will take generative AI from concept to production and help set the standard for semantic consistency across systems.

You will partner closely with stakeholders to turn complex business needs into measurable outcomes.

You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions.

If you enjoy solving hard problems with real impact, this is the opportunity.

Job Responsibilities


* Build generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment with measurable outcomes


* Design context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experience


* Lead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle management


* Partner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraints


* Define semantic integration patterns across data pipelines, application programming interfaces (APIs), data contracts, and experience layers to resolve semantic conflicts


* Establish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems


* Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approaches


* Build and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and machine learning operations


* Implement responsible AI practices, model risk controls, and governance aligned to regulated environments


* Mentor engineers and data scientists, raising the bar on engineering rigor, reuse, and continuous improvement across the team

Required Qualifications, Capabilities, and Skills


* Master's degree in a data science-related discipline and eight years of industry experience, or 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 maintaina...