Join a team driving innovation in AI/ML and data platforms. Grow your engineering skills while delivering impactful solutions.
As a Lead Software Engineer at JPMorgan Chase within the AI/ML Data Platforms team, you will design and build products, services, and developer tooling that enhance the reliability and operability of our platforms at scale. You will partner closely with platform, data engineering, and SRE teams to ensure solutions are observable, resilient, and production-ready. We value coding excellence, system design, and engineering best practices, fostering a collaborative and inclusive team culture.
Job responsibilities
- Design and build software services and automation tooling to improve platform scalability, reliability, and developer experience across technologies such as Databricks, Snowflake, AWS, and Kubernetes.
- Own end-to-end software delivery including requirements, design, implementation, testing, deployment, and production support for critical engineering tools and services.
- Write secure, high-quality, maintainable production code with appropriate unit and integration tests, code reviews, and CI validations.
- Develop libraries, APIs, CLIs, and workflow automation to reduce operational toil and standardize engineering patterns.
- Build and enhance observability features in delivered software to speed up troubleshooting and reduce time-to-recovery.
- Perform root cause analysis on incidents impacting owned services and tools, delivering durable fixes.
- Collaborate with engineering and data teams to improve CI/CD, release safety, and deployment practices with a focus on automation and operational excellence.
- Mentor and guide engineers on software craftsmanship, system design, and reliability practices within a product engineering mindset.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
Required qualifications, capabilities and skills
- Formal training or certification on software engineering concepts and 8+ years applied experience
- Strong coding proficiency in Python and/or Java with ability to design, implement, debug, and optimize production systems
- Strong grasp of data structures, algorithms, OOP/design patterns, and system design for scalable services
- Proven experience building production-grade services or platforms, including APIs, background workers, and automation frameworks
- Hands-on experience with testing practices, test automation, and CI/CD pipelines
- Practical knowledge of observability and reliability principles including basic monitoring concepts, SLI/SLO awareness, and incident response hygiene
- Demonstrated ability to reduce toil via automation and deliver developer-facing tooling that improves reliability and productivity
- Solid understanding of secure software development and risk controls including secure coding, dependency hygiene, secrets handling, and least privilege
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities and skills
- Experience working with data platforms and distributed processing such as Spark or PySpark, batch or stream pipelines
- Familiarity with cloud-native services and infrastructure concepts including AWS fundamentals, containers, and Kubernetes basics
- Exposure to Databricks and/or Snowflake administration or platform enablement
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