Leads development of secure, high-performance, low-latency Java systems within an agile engineering team. Designs architecture, writes and reviews code and automated tests, translates business requirements into technical solutions, and drives architectural reviews and delivery. Works with distributed systems, messaging, databases, and networking technologies while assessing risk and compliance. Promotes responsible AI-assisted engineering practices, mentors team members, contributes to recruitment, and supports technical innovation.
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 JPMorgan Chase within the Commercial & Investment Bank, 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.
Job responsibilities
- Designs and develops robust, high-performance, latency-sensitive code; incorporates security requirements; and reviews code written by team members using software engineering best practices.
- Writes secure, high-quality code and automated tests.
- Works closely with Product Owners, Solutions Analysts, Technical Architects, and Senior Engineers to define the best technical design and development approach.
- Translates business requirements into technical solutions, recommends alternative technical and business approaches, and leads engineering efforts to meet ambitious timelines with optimal solutions.
- Helps refine the technical aspects of epics in collaboration with Solutions Analysts.
- Produces architecture and design artifacts for complex applications while ensuring design constraints are met by code.
- Drives architectural reviews, code reviews, and business demos.
- Assesses compliance, risks, and vulnerabilities to ensure all systems and baselines are operationally sound, performant at scale, and exceed customer expectations.
- 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.
- Contributes to the ongoing development of the team, including recruitment, paired programming, peer review, and mentoring.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Excellent understanding of Java 17+ fundamentals and frameworks such as Spring Boot
- Strong knowledge of object-oriented programming paradigms, multithreading, messaging technologies, and computer networks
- Experience with low-latency programming techniques and technologies such as Inter-Process Communication (IPC), memory-mapped files, and ring buffers
- Experience with gRPC and Google Protocol Buffers
- Experience working on distributed systems, including detailed knowledge of distributed-systems design patterns
- Detailed knowledge of relational databases (e.g., PostgreSQL, CockroachDB) including resiliency, recoverability, scalability, and security
- Experience with distributed messaging technologies such as Kafka
- 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
- Experience with diagramming tools (PlantUML, Lucid, Draw.io, Visio) and technical writing; passion for technical innovation, staying current with emerging technologies, understanding the SDLC and agile methodologies, and demonstrated coaching/mentoring experience
Preferred qualifications, capabilities, and skills
- Hands-on experience with another statically compiled language such as Golang, Rust, or C++.
- Experience with Apache Ignite, GridGain, Hazelcast, or Redis.
- Experience with infrastructure as code software such as Terraform.
- Awareness of key architectural patterns/approaches, algorithms, data structures, cryptography, security protocols (e.g., TLS and OAuth/OIDC), secure system design and Threat Modelling.
Similar Jobs
Financial Services
Leads development of secure, high-performance, low-latency Java applications and distributed systems. Translates business requirements into technical designs, produces architecture artifacts, conducts code and architecture reviews, and ensures scalability, resilience, security, and compliance. Mentors engineers, supports recruitment, drives agile delivery, and promotes responsible AI-assisted development, automated testing, and engineering best practices.
Top Skills:
Apache IgniteC++CockroachdbDraw.IoGoGoogle Protocol BuffersGridgainGrpcHazelcastJava 17+KafkaLucidPostgresRedisRustSpring BootTerraformVisio
Fintech • Legal Tech • Software • Financial Services • Cybersecurity • Data Privacy
Supports payment-system workflows across client onboarding, product enhancements, production support, testing, and releases. Gathers and documents requirements, creates user stories and process flows, coordinates cross-functional stakeholders, supports UAT and defect resolution, investigates production issues, tracks risks and dependencies, and manages operational handovers. Requires flexibility for urgent production issues and planned releases.
Top Skills:
AgileAzure DevopsJIRA
Artificial Intelligence • Fintech • Hardware • Information Technology • Sales • Software • Transportation
Develop and deploy AI and deep-learning models for visual, audio, and real-world perception systems. Responsibilities span model research, data and evaluation pipeline design, robustness analysis, real-time and resource-constrained deployment, and production troubleshooting across edge and cloud environments. Collaborate with firmware, platform, and product teams to deliver reliable systems. Experience with Python, PyTorch, a systems language, agentic tools, and optionally LLMs, embedded ML, video understanding, retrieval, and distributed training is valued.
Top Skills:
AnyscaleC++Distributed TrainingEmbedded MlGoLlmsOnnx RuntimePythonPyTorchRayRedashSnowflakeSnpeTensorrtTfliteVlms
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
