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.
- 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.
- Build and maintain Python services, scripts, and pipelines for data/AI use cases.
- Write efficient SQL for analysis, data validation, debugging, and performance tuning.
- Rapidly triage incidents: reproduce issues, isolate root cause, and implement fixes.
- Develop and iterate on AI applications (e.g., LLM-powered workflows, retrieval, evaluation).
- Design and implement agentic systems (tool-using agents, orchestration, guardrails, memory patterns where appropriate).
- Create monitoring/observability: logging, metrics, traces, and alerting for AI and data services.
- 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.
- Document system behavior, known failure modes, and support procedures
- 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 a s 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.
- 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
- 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)