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 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 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)