Lead Software Engineer - Python, Sql

JPMorganChase; Maharashtra; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

JPMorgan Chase in India leads an exciting opportunity for a Lead Software Engineer within the Credit Technology team. You will build real-time Risk/PnL platforms for Bonds, Loans, Credit Derivatives and Exotics, delivering secure, scalable microservices with low latency and high throughput.

You will mentor engineers, drive AI-assisted development practices, and collaborate with quant, risk, and production teams to meet SLAs and governance standards.

Qualifications

• Formal training or certification on software engineering concepts with 12+ years applied experience.
• Extensive hands-on experience delivering Python services in production including design, development, testing, troubleshooting and operational support.
• Strong knowledge of data structures, algorithms, concurrency and software design principles; able to lead design discussions and document architecture.
• Experience across the full SDLC: CI/CD, testing automation, release management, and production support.
• Strong SQL skills and experience with relational databases; ability to design schemas and write performant queries.
• Proven ability to build secure, stable, maintainable systems in a large enterprise environment with SDLC governance.
• Demonstrated experience leading the use of approved AI-assisted software development tools with validation of outputs.

Responsibilities

• Build real-time Risk/PnL systems for loans, derivatives, exotics and other products; include intraday Greeks, VaR inputs, explain/attribution and stress runs.
• Design and deliver low-latency, high-throughput services that publish risk and PnL data to front-office consumers with clear SLAs and observability.
• Develop distributed microservices and event-driven pipelines that consume market data and trades to produce risk measures and serve APIs/UI.
• Drive AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes with secure coding and automated testing.
• Apply enterprise SDLC tools and automation to improve value realization and governance of AI-assisted development.
• Own technical design and implementation with awareness of data contracts, schema evolution, and failure modes.
• Apply engineering rigor in test strategy, performance profiling, capacity planning, resiliency patterns, and secure coding.
• Drive production excellence: incident triage, root cause analysis, runbooks, automated recovery and reliability improvements.
• Collaborate with stakeholders to translate business needs into technical requirements and deliver iteratively.
• Mentor engineers, contribute to code reviews, raise the bar on architecture and foster an inclusive team culture.

Skills

Python

Real-time systems

Low-latency design

Distributed microservices

SDLC governance

AI-assisted development

Performance tuning

Concurrency

API design

Troubleshooting

Tools

CI/CD

Job description

JOB DESCRIPTION

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.

• Mentor engineers, contribute to code reviews, raise the bar on architecture and craftsmanship, and foster an inclusive team culture.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 12+ years applied experience

• Extensive hands-on experience delivering Python services in production (design, development, testing, troubleshooting, and operational support).

• Strong knowledge of data structures, algorithms, concurrency, and software design principles; able to lead design discussions and document architecture.

• Experience across the full SDLC: CI/CD, testing automation, release management, and production support.

• Strong SQL skills and experience with relational databases; ability to design schemas and write performant queries.

• Proven ability to build secure, stable, maintainable systems in a large enterprise environment (controls, auditability, SDLC governance).

• 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 real-time systems: messaging, streaming, caching, and low-latency APIs.

• Proficiency with profiling and performance tuning (CPU/memory/IO), and designing for throughput, backpressure, and graceful degradation.

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