Lead Software Engineer, Commodities Technology — Energy

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 Commodities Technology (Energy), 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. JPMorganChase Commodities Technology is hiring a hands-on Local Development Lead in Singapore to build and run mission-critical software for the Energy franchise. You will partner closely with front office traders, Quants/QR, Sales, Middle Office, and Production Management to deliver high-quality capabilities across trading, risk, P&L, inventory, and logistics workflows in fast-moving markets (e.g., power, natural gas/LNG, emissions/environmental products). Job Responsibilities Lead delivery for Singapore Energy tech: own end-to-end execution from requirements and design through build, test, release, and production support. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems. Develops secure and high-quality production code, and reviews and debugs code written by others; sets a high bar for code review quality and engineering craftsmanship. Build inventory & logistics capabilities on the Athena platform to support expanding physical/structured energy activities (state, movements, costs, constraints, controls, and integrations). Engineer for reliability and performance: design resilient, observable services and workflows with clear failure modes, safe rollouts/rollbacks, and strong operational readiness. Raises team standards across testing strategy, CI/CD discipline, documentation/runbooks, and incident/post-incident practices. 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. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems. Mentors and grows engineers: develops technical capability, coaches on ownership and operating discipline, and helps shape hiring/onboarding as the local team evolves. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture. Required Qualifications, Capabilities, and Skills Bachelor’s degree in Computer Science/Engineering/Math (or equivalent experience). Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability. Proficient in all aspects of the SDLC; advanced understanding of agile/modern engineering practices including CI/CD, application resiliency, and security. Advanced in one or more programming languages; strong coding skills in at least one of: Python, TypeScript, Scala, C++, C#; willingness to become highly proficient in Python. Experience designing large-scale systems with complex data models and integrations (object and/or transactional databases). Proven leadership in raising engineering quality: reviews, testing, release discipline, and operational excellence. Strong fundamentals in data structures, algorithms, and system/enterprise architecture. 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. In-depth knowledge of the financial services industry and its IT systems. Practical cloud-native experience. Preferred Qualifications, Capabilities, and Skills Commodities/energy trading and risk domain experience (power/gas/LNG/emissions) and/or physical logistics/inventory systems. Experience building software used directly by traders or tightly coupled to pricing/risk/P&L. Practical experience introducing AI/agentic development workflows with robust guardrails and measurable quality outcomes.