What are the responsibilities and job description for the Applied AI/ML Engineer position at JPMorgan Chase?
The global private Bank Core Technology AI Team is delivering production quality AI solutions to multiple lines of business in Global Private Bank. We are designing solutions in environments where trust and explainability are critical. Collaboration across business and tech is core to our culture and we value people who can connect the dots across disciplines and help bring AI to life inside production systems.
As a Applied AIML Engineer , you will build effective, scalable, and modern analytical solutions for various banking domain problems and deploy them into production business workflows. This is an exciting opportunity to work alongside a world-class group of Data Scientists and Machine Learning Engineers and have profound influence on the business and technology processes of the firm. You will have broad areas of ownership including but not limited to stakeholder engagement, data mining, insights delivery, training and deployment of machine learning/LLM solutions as well as the ability to influence entire organizations. All in a modern data and development environment.
Job Responsibilities
- Design, develop, and deploy LLM-based and traditional ML solutions that enhance real-world business processes (e.g., document understanding, summarization)
- Serve as a bridge between data science, engineering, and business—engaging directly with stakeholders to frame problems, interpret results, and drive adoption
- Deeply understand business data and workflows, uncover actionable insights, and surface opportunities for transformation using AI
- Contribute to best practices around model validation, evaluation, safety, and responsible AI
- Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Required Qualifications, Capabilities, And Skills
- Master’s or PhD in Computer Science, Machine Learning, Data Science, or a related field
- 5 years of experience delivering ML/AI solutions in production, including deep familiarity with Python-based ML/LLM stacks (e.g., PyTorch, Hugging Face, Scikit-learn)
- Strong track record developing and scaling LLM or NLP systems—document extraction, QA, summarization, embeddings, etc.
- Practical understanding of model deployment, evaluation, and lifecycle management (not just experimentation)
- Excellent communication skills; ability to explain complex technical concepts to non-technical stakeholders and senior leaders
- Comfortable working in ambiguous problem spaces and navigating across technical and business domains
- Detail-oriented, thoughtful, and mission-driven—you care about making things work, not just making things interesting
Preferred Qualifications, Capabilities, And Skills
- Experience working in financial services, especially regulated environments
- Familiarity with multi-agent systems or chaining LLMs with traditional systems
- Exposure to model monitoring, fairness, explainability, or AI governance
