Lead Software Engineer - Python, SQL

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank’s Credit Technology team, you will join an agile engineering team building real-time and end-of-day Risk & PnL platforms for Bonds, Loans, Credit Derivatives, Exotics products etc.

You will design and deliver secure, resilient, low-latency, and scalable services that power front-office risk, trading, and management reporting workflows.

You will be part of technical delivery across multiple components, drive engineering standards, and partner closely with quant, trading, risk, and production management teams.

Job responsibilities

• Build real-time Risk/PnL systems supporting Loans, Credit Derivatives, Exotics products etc (e.g., intraday Greeks/sensitivities, VaR inputs, explain/attribution, scenario and stress runs).
• Design and deliver low-latency, high-throughput services that publish risk and PnL to front-office consumers with clear SLAs, observability, and operational readiness.
• Develop distributed microservices and event-driven pipelines that consume market data, trades, and reference data; produce risk measures; and serve APIs/UI consumers.
• 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.
• Own technical design and implementation with awareness of upstream/downstream dependencies, data contracts, schema evolution, and failure modes.
• Apply strong engineering rigor: test strategy, performance profiling, capacity planning, resiliency patterns, and secure coding.
• Drive production excellence: incident triage, root cause analysis, runbooks, automated recovery, and measurable reliability improvements.
• Collaborate with stakeholders to translate business needs into clear technical requirements and deliver iteratively.