About this role
Our goal is to build the next generation of AI: autonomous agents that can reason, plan, act, and learn to solve critical problems for an industry-leading financial institution. You will join a team transforming operations by leveraging advancements in agent-based AI and modern generative AI techniques.
As an Applied Artificial Intelligence and Machine Learning Associate at JPMorganChase within the Applied AI Research team in the Commercial & Investment Bank, you will contribute to building autonomous agent systems that solve high-impact business problems. You will help translate business needs into measurable outcomes by supporting applied research, model development, and enterprise-ready delivery. You will work in a collaborative environment that values ownership, learning, and responsible adoption of modern AI.
Job responsibilities
- Build and enhance generative AI and agent-based solutions that support automation of complex operational workflows
- Translate business problems into clear technical approaches, success metrics, and experiment plans with guidance from senior team members
- Develop and test agent workflows (planning, tool-use, and orchestration) to improve task reliability and measurable performance
- Implement reusable components (services, libraries, and reference patterns) that accelerate applied AI delivery across teams
- Execute rigorous evaluation practices, including offline/online testing, error analysis, and quality measurement to drive improvements
- Partner with stakeholders across business and technology teams to gather requirements, communicate tradeoffs, and support production readiness
- Contribute to responsible scaling by considering reliability, security-mindedness, and operational constraints in design and implementation
Required qualifications, capabilities and skills
- Formal training or certification on applied artificial intelligence and machine learning concepts and 2+ years applied experience
- Bachelor’s degree in Computer Science, Engineering, Statistics, or a related quantitative field (or equivalent practical experience)
- Hands-on experience building production-grade software in Python, including testing, refactoring, and version control best practices
- Experience applying machine learning methods to real-world problems and evaluating results using clear, reproducible metrics
- Experience deploying or supporting services in a cloud environment with attention to reliability, observability, and latency
- Strong fundamentals in software engineering (data structures, algorithms, and object-oriented design) and applied problem solving
- Strong communication skills and ability to collaborate effectively with technical and non-technical partners
Preferred qualifications, capabilities and skills
- Experience building or evaluating autonomous agent systems (planning, tool-use, orchestration, or multi-agent coordination)
- Experience with cloud-based machine learning platforms, such as Amazon Web Services (AWS) SageMaker or Amazon Bedrock
- Experience building and evaluating large language model solutions using structured evaluation approaches and safety guardrails
- Publications, open-source contributions, or other evidence of applied research impact in machine learning or generative AI
- Familiarity with financial services domains and operational processes, including risk-aware design and production constraints