Lead Applied AI & Machine Learning Engineer

JPMorgan Chase & Co. • Plano, TX

17h

About The Position

As an Applied AI and Machine Learning Lead in JPMorganChase’s Corporate Technology Data Science and AI team, you will build enterprise AI solutions that improve decision-making, automate work, and serve internal customers. You will take generative AI from concept through production, help establish semantic consistency across systems, and partner with stakeholders to turn complex business needs into measurable outcomes. The role involves mentoring talent and influencing technical direction across Corporate Technology and supported Corporate Functions.

You will develop generative AI, agentic AI, and large language model solutions, establish enterprise semantic modeling and integration practices, and enable intelligent workflows using ontology-driven context and semantic reasoning. You will also build and maintain machine learning pipelines and frameworks, apply responsible AI practices and model risk controls for regulated environments, and support consistent, trusted analytics across business intelligence, machine learning, and transactional systems.

Requirements

• 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.
• Ownership and attention to detail in ambiguous, complex problem spaces.
• Ability to work independently and collaborate 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 for 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.
• Partner with stakeholders to translate complex business needs into measurable outcomes and influence technical direction across Corporate Technology and supported Corporate Functions.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

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