Software Engineer Iii, Global Equity Portfolio Management Technology

JPMorgan Chase JPMorgan Chase · Banking · Singapore · Asset & Wealth Management

Software Engineer III role focused on supporting and enhancing critical portfolio management applications within JPMorgan Chase's Asset Management division. The role involves designing and engineering scalable systems, architecting event-driven capabilities, and driving engineering best practices. A significant aspect is the championing and application of AI-assisted development tools and patterns to improve code quality, delivery speed, and productivity, while emphasizing responsible AI use and critical evaluation of AI outputs.

What you'd actually do

  1. Support critical portfolio management applications in production. Partner directly with Portfolio Managers (PMs) to triage incidents, restore service quickly, and drive long-term stability improvements.
  2. Design and engineer scalable, high-performance systems spanning portfolio construction through portfolio implementation workflows.
  3. Architect and implement event-driven/streaming capabilities (e.g., Kafka) to ingest market data, signals, reference data, and intraday portfolio events with strong observability, replay, and data quality controls.
  4. Champion AI-assisted development practices and help standardize/practice adoption of AI engineering patterns across the team.
  5. Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.

Skills

Required

  • Formal training or certification on software engineering concepts and 3+ years applied experience.
  • Bachelor’s Degree in Computer Science or equivalent.
  • Proven track record building and delivering highly scalable platforms. (Experience with multithreaded, concurrent, distributed systems)
  • Strong expertise in modern UI technologies (JavaScript/TypeScript) and core Java with solid object-oriented design fundamentals. Proven ability to write clean, maintainable, well-tested code.
  • Hands-on enterprise development experience with solid understanding of software design principles, especially event-driven architecture and the ability to deep-dive/debug complex production codebases.
  • Ability to partner closely with business stakeholders, product leads, and cross-functional technology teams to translate complex needs into actionable roadmaps and measurable outcomes.
  • Strong problem-solving skills, sound engineering judgment, and comfort operating in ambiguity.
  • AI practitioner with hands-on experience using AI coding/agent tools (e.g., Copilot/Codex/Claude Code)
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Nice to have

  • Experience in financial services or Portfolio Management technology.
  • Knowledge of distributed systems and microservices architecture.
  • Practical cloud-native experience (CI/CD, infrastructure-as-code, observability) is a plus
  • Interest in financial markets and portfolio management workflows.

What the JD emphasized

  • AI practitioner with hands-on experience using AI coding/agent tools (e.g., Copilot/Codex/Claude Code)
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.