Software Engineer III - AI/ML Platform Reliability

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.

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