About the role
structured by ORIBuild and deploy agent-based AI solutions that automate operations and improve decision-making at scale. 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.
What you will do
- 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
What they are looking for
- 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
Nice to have
- 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
Full posting text
Build and deploy agent-based AI solutions that automate operations and improve decision-making at scale.
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