We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Risk Corporate Technology, you are an integral part of an agile 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 conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- minimum of 8 years industry experience.
- Strong Python engineering skills (data handling, APIs, concurrency basics, packaging).
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Must have working knowledge in in various observability tools such as OTEL, Grafana, Splunk and Dynatrace
- Strong SQL skills (joins, window functions, query optimization, troubleshooting bad data).
- Proven ability to debug quickly and work through ambiguous production issues.
- Experience delivering production-grade software (testing, code reviews, version control).
- Strong communication skills—can explain root cause and fixes clearly.
- 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.
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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 building AI solutions using LLMs (prompting, RAG, evaluation, safety/quality checks).
- Experience with agent frameworks/orchestration patterns (tool calling, planning/execution loops).
- Familiarity with data platforms/warehouses and pipelines (e.g., Airflow or similar schedulers).
- Observability tooling experience (structured logging, metrics, tracing).
- Performance tuning experience for Python services and SQL workloads.
- Cloud/container experience (Docker, Kubernetes, or managed equivalents)