Sr Lead Software Engineer - Digital Assets / Blockchain

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Asset & Wealth Management

Senior Lead Software Engineer for JPMorgan Chase's Digital Assets Team, focusing on building and enhancing technology products within the blockchain space. The role involves executing creative software solutions, developing secure code, and leading evaluation sessions. A key responsibility is driving the adoption and governance of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while ensuring responsible AI use and adherence to security standards.

What you'd actually do

  1. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  2. Develops secure high-quality production code, and reviews and debugs code written by others
  3. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  4. 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
  5. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Design and develop scalable, secure services utilizing Java, Spring Boot, and TDD
  • Collaborate with platform teams to enhance developer experience, toolchains, and cloud-hosted blockchain services.
  • Work closely with Product, Operations, and Core Blockchain teams to innovate and execute transformative blockchain use cases.
  • Strong object-oriented programming language background in Java or more of the following languages: Go, Rust or JavaScript
  • Proficiency with enterprise development toolchains: Git, Jenkins, CI/CD pipelines, automated testing
  • Demonstrable practical cloud native experience, in particular AWS and Kubernetes.
  • Practical experience delivering system design, application development, testing, and operational stability
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls

Nice to have

  • Hands-on knowledge of end-2-end development in web3 ecosystem – such as smart contract development, deployment, interaction with RPC providers, integrating with key management systems is a plus
  • Exposure to building on public or permissioned blockchain platforms (e.g., Ethereum/EVM, Hyperledger, Solana, Polkadot, Cosmos, Avalanche, Canton) is a plus
  • Thorough understanding of cryptographic protocols and blockchain implementations
  • Good understanding in key management, custody solutions and cryptographic fundamentals is a plus

What the JD emphasized

  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls

Other signals

  • AI-assisted engineering practices
  • AI-assisted code review/refactoring
  • test acceleration
  • release readiness
  • incident/root-cause analysis
  • AI-assisted development and automation capabilities
  • enterprise-authorized AI-assisted software development tools
  • responsible AI use in engineering workflows