Shape the future of customer-facing financial experiences by leading applied artificial intelligence and machine learning solutions that deliver real business impact. Join a team that values engineering excellence, modern architecture, and strong collaboration across product, data, and risk partners. Bring your expertise to a role with broad mobility, meaningful technical ownership, and opportunities to influence platform standards and adoption.
As an Applied AI ML Lead at JPMorgan Chase within the Consumer and Community Banking - Architecture and Engineering team, you are an integral part of a team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
In this role, you will partner closely with product, engineering, data, and control stakeholders to identify high-value machine learning use cases and take them from concept to production. You will balance delivery speed with security, resiliency, and responsible use expectations, ensuring solutions are scalable and supportable. You will also help teams adopt repeatable patterns for model development, deployment, and monitoring. Success requires strong technical judgment, clear communication, and the ability to lead through influence in a fast-moving environment.
Job Responsibilities
- Design end-to-end software and machine learning solutions that are secure, resilient, and scalable for production use.
- Develop high-quality production code in Python and perform thorough code reviews to improve correctness, security, and maintainability.
- Build and operationalize machine learning models, including generative approaches where applicable, from experimentation through deployment and ongoing support.
- Define evaluation, testing, and monitoring practices for model and application performance, reliability, and drift to sustain production outcomes.
- Automate recurring remediation and operational tasks to improve stability, reduce incidents, and streamline support.
- Lead technical solutioning across multiple components and services, aligning architecture decisions with business requirements and platform constraints.
- Drive workflow automation opportunities using machine learning to improve customer or employee experiences while maintaining appropriate controls.
- Evaluate external vendors, startups, and internal solutions by probing architecture, technical fit, and integration feasibility within existing systems.
- Mentor engineers through communities of practice and day-to-day guidance, reinforcing an inclusive, respectful, and high-performing team culture.
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts and 5+ years of applied software engineering experience.
- Hands-on experience delivering system design, application development, testing, and production support for customer- or business-critical systems.
- Professional experience developing in Python, including building and supporting machine learning workloads.
- Experience implementing automation and continuous delivery practices in a production engineering environment.
- Working knowledge of the full software development life cycle, including design, build, test, release, and operate.
- Experience building or integrating machine learning solutions, including generative model use cases in production or pre-production environments.
- Experience working with cloud-native technologies and deploying workloads to a public cloud environment (for example, Amazon Web Services).
- Experience working in financial services technology or similarly regulated environments with security and control expectations.
Preferred qualifications, capabilities, and skills
- Experience designing or implementing agent-based artificial intelligence solutions (for example, tool-using or multi-step reasoning workflows).
- Experience fine-tuning or adapting language models, including small language models and large reasoning models, for domain-specific use cases.
- Familiarity with model evaluation techniques for generative solutions (for example, automated quality checks and human-in-the-loop review patterns).
- Experience partnering with governance, risk, or control stakeholders on responsible model deployment and ongoing monitoring.