Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Senior Lead Software Engineer at JPMorgan Chase within Commercial & Investment Bank's Kinexys Digital Payments Technology, you play a crucial role in an agile team dedicated to enhancing, building, and delivering trusted, market-leading technology products that are secure, stable, and scalable. 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.
Job responsibilities
- Defines and drives the roadmap; make high-impact design decisions on distributed systems, microservices, APIs, and data pipelines.
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Collaborate with Product Owners, SREs, Cybersecurity, Risk, and Compliance to deliver features that meet business and regulatory requirements.
- Mentor and grow a team of senior, mid-level, and junior engineers, conduct code reviews, design reviews, and career development conversations.
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Write, review, and mentor on production-grade code in Java, Python, Go, or similar; champion clean code, TDD, and modern engineering practices.
- Evaluate emerging technologies (event-driven architecture, AI/ML platforms, real-time streaming, etc.) and drive their adoption where appropriate.
- 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.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years of applied experience in server-side / backend engineering.
- Hands-on experience with blockchain platforms such as Ethereum, Hyperledger Besu, Hyperledger Fabric, Quorum, Corda, or Polygon
- Expertise in one or more backend languages: Java, Go, C++, Scala, or Kotlin.
- Deep knowledge of distributed systems, microservices architecture, RESTful and gRPC APIs, and event-driven design (Kafka, RabbitMQ, etc.).
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
- Experience with cloud platforms (AWS), containers (Docker, Kubernetes), and CI/CD pipelines (Jenkins, Spinnaker).
- Solid understanding of performance tuning, caching strategies and observability (Prometheus, Grafana, Splunk, Dynatrace). Proven track record of leading large-scale engineering programs and mentoring senior engineers.
- Experience with AWS ecosystem and application migrations from on-premises and hybrid solutions to fully cloud-native applications.
- In-depth knowledge of the financial services industry and their IT systems.
- 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
Required qualifications, capabilities, and skills
- Experience operating in highly regulated environments (financial services, healthcare, etc.) is strongly preferred.
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