Lead Applied AI & Machine Learning Engineer

JPMorganChase • Plano, TX

1d

About The Position

The Lead Applied AI & Machine Learning Engineer will develop enterprise AI solutions that improve decision-making, automate work, and serve internal customers. The role takes generative AI solutions from concept through production, establishes approaches for semantic consistency across systems, and partners with stakeholders to turn complex business needs into measurable outcomes. It also mentors talent and influences technical direction across Corporate Technology and supported Corporate Functions.

The position is part of the Corporate Technology Data Science and AI team. It combines applied AI and machine learning engineering with enterprise semantic modeling, ontology governance, intelligent workflow development, and responsible AI practices in regulated environments.

Requirements

• A 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.
• 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, including the ability to explain complex concepts to technical and non-technical stakeholders.
• Demonstrated ownership and attention to detail in ambiguous, complex problem spaces.
• Ability to work independently while collaborating effectively across product, engineering, data, and business partners.

Nice To Haves

• Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance.
• Experience implementing retrieval-augmented generation, tool use, and evaluation strategies for large language model applications.
• Familiarity with responsible AI techniques, including bias testing, explainability approaches, and model monitoring standards.
• Experience designing scalable architectures for real-time or near-real-time inference and intelligent workflow orchestration.
• Experience influencing cross-functional technical direction and mentoring engineers through design reviews and delivery execution.

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 representing business entities, relationships, rules, and constraints.
• Define semantic integration patterns across data pipelines, 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, promoting engineering rigor, reuse, and continuous improvement across the team.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior