Lead Applied AI & Machine Learning Engineer
JPMorgan Chase2 days ago
Responsibilities
- Build generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment.
- 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.
- Create scalable ontologies representing business entities, relationships, rules, and constraints in partnership with domain experts.
- Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers.
- Establish and govern a unified semantic layer supporting trusted analytics across business intelligence, machine learning, and transactional systems.
- Enable intelligent workflows and AI agents through ontology-driven context, semantic reasoning, and orchestration.
- 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 for regulated environments.
- Mentor engineers and data scientists and influence technical direction through engineering rigor, reuse, and continuous improvement.
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.
- Experience developing and deploying machine learning and generative AI solutions using Python.
- Experience writing and maintaining production-quality code with 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 for explaining complex concepts to technical and non-technical stakeholders.
- Demonstrated ownership and attention to detail in ambiguous and complex problem spaces.
- Ability to work independently while collaborating across product, engineering, data, and business teams.
- Preferred experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance.
- Preferred experience with retrieval-augmented generation, tool use, and evaluation strategies for large language model applications.
- Preferred familiarity with responsible AI techniques, including bias testing, explainability, and model monitoring standards.
- Preferred experience designing scalable architectures for real-time or near-real-time inference and intelligent workflow orchestration.
- Preferred experience influencing cross-functional technical direction and mentoring engineers through design reviews and delivery execution.
Benefits
- Competitive total rewards and benefits package, with details provided during the hiring process.
- Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash or forfeitable equity.
- Benefits may include comprehensive health care coverage, on-site health and wellness centers, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching.
Tech Stack
Categories
About JPMorgan Chase
JPMorgan Chase provides consumer and commercial banking, payments, credit card, wealth management, and corporate and investment banking services to individuals, businesses, institutions, and governments. The public company (NYSE: JPM) earns revenue from interest, fees, trading, and asset management across operations in more than 100 markets. Headquartered in New York City with roots dating to 1799, it serves retail customers and prominent corporate and government clients through brands including Chase and J.P. Morgan.