Lead Software Engineer - Python, Observability

JPMorgan Chase & Co.; Houston (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer in Risk Corporate Technology to drive critical AI-enabled engineering across multiple business functions.

You will contribute as a core technical member, building secure, scalable software and data pipelines while advancing monitoring and reliability. The role emphasizes leadership in adopting enterprise AI-assisted practices, strong Python and SQL capabilities, and collaboration across teams to deliver trusted tech products in a demanding

Qualifications

• Formal training or certification on software engineering concepts with 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 and CI/CD, application resiliency, and security.
• Working knowledge of observability tools such as OTEL, Grafana, Splunk and Dynatrace.
• Strong SQL skills (joins, window functions, performance tuning).
• Proven ability to debug quickly and handle production issues.
• Experience delivering production-grade software with testing and code reviews.

Responsibilities

• Executes creative software solutions with ability to think beyond routine approaches.
• 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 fix.
• Develop and iterate on AI applications (LLM-powered workflows, retrieval, evaluation).
• Design and implement agentic systems (tool-using agents, orchestration, memory patterns).
• Create monitoring/observability: logging, metrics, traces, and alerting for AI/data services.
• Drives team adoption of enterprise AI-assisted engineering practices and validation standards.

Skills

Python

SQL

CI/CD

Security

Observability

AI tooling

Debugging

Communication

Leadership

Agile methodologies

Tools

Docker

Kubernetes

Airflow

OTEL

Grafana

Splunk

Dynatrace

Job description

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 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)