Lead Software Engineer - Python, Observability

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

• 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

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.

• 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)