Are you passionate about building resilient, scalable systems that power the future of AI? At JPMorganChase, we're pushing the boundaries of what's possible with artificial intelligence and machine learning — and we need engineers like you to help us do it reliably, securely, and at scale.
As a Software Engineer III at JPMorganChase within the AI/ML Data Platforms organization, you will be a key member of the Reliability Engineering team, contributing to the design and delivery of trusted, market-leading technology products. You will apply your technical expertise and problem-solving skills to enhance the reliability and scalability of AI/ML platforms, build reusable services and tooling, and partner across teams to unblock high-impact AI use cases. This is an opportunity to shape how the firm delivers AI capabilities — with operational excellence at the core.
Responsibilities:
- Design and implement solutions to enhance the reliability and scalability of AI/ML platforms and applications to accommodate fast-growing demands.
- Own NFRs and develop tooling for observability, security, resilience, infrastructure management and operations excellence.
- Build and maintain scalable infrastructure to support the deployment and operation of large-scale AI platforms and apps.
- Build strong cross-functional relationships that foster engagements across the organization and deliver solutions to user problems.
- Participates in on-call rotations and escalation workflows, Debug and solve issues in a production environment, take full ownership of problems, develop solutions.
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
- Develops secure high-quality production code, and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and proficient applied experience.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
- Advanced proficiency in programming with Python.
- Proficiency in all aspects of the Software Development Life Cycle.
- Experience with infrastructure-as-code and cloud-native delivery practices, including tools such as Terraform, containers, Kubernetes, CI/CD pipelines, and automated deployment workflows.
- Experience in designing and developing large-scale distributed systems and cloud-native architecture.
- Experience building large scale infrastructure and and cloud-native delivery practice in Google Cloud, AWS, or Azure and Terraform.
- Extensive experience implementing advanced observability using tools like Open Telemetry, Dynatrace, Grafana, and/or cloud-native services.
- Systematic problem-solving and troubleshooting skills in a complex system.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
- Prior experience working in AI Cloud Infrastructure.
- Prior experience developing GenAI Apps or AI Agents.
- Previous experience as an Infrastructure or Platform Software Engineer in a dynamic technology company or startup.
- Self-managed, self-motivated with strong sense of ownership, urgency, and drive