Lead Software Engineer - Python, sql/plqsl, Automation
On-siteMumbai, Maharashtra, India
Job Summary
Execute creative software solutions, design, and technical troubleshooting while leading end-to-end P1/P2 production incident response, triage, and recovery. Drive problem management by running RCA sessions, defining corrective actions, and prioritizing fixes with engineering teams. Lead TOIL reduction through Python and SQL/PLSQL automation scripts, tooling, and workflows. Establish consistent validation standards for secure coding, peer review, and automated testing while promoting reusable patterns across the team. Lead evaluation sessions with external vendors and startups to probe architectural designs and drive outcomes-oriented adoption of new technologies. This role supports JPMorganChase's Commercial & Investment Bank within an agile team focused on delivering secure, scalable technology products.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Strong hands-on engineering skills in Python and database systems (SQL/PLSQL)
- track record of building automation and diagnostic tooling
- Exposure to AWS cloud and Kafka
- Proficiency in automation and continuous delivery methods
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Desired Qualifications
- Design and deliver self-service operational tooling (including UI-based run/operate screens) using AI agents/Copilot-style capabilities to improve support throughput and consistently
- 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
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