Lead Software Engineer - Java Low Latency
JP Morgan Services India Pvt Ltd
Work from Office
Full Time
Min. 5 yearsJob Description
Lead Software Engineer - Java Low Latency
As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank, Payments Technology, 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, incorporate security requirements and reviewing 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 design the best technical design and approach for technical development. Translates business requirements into technical solutions, recommend alternative technical and business approaches, and lead engineering efforts to meet ambitious timelines with optimal solutions.
- Helps refine the technical aspects of epics in collaboration with the Solutions Analysts.
- Translates business requirements into technical solutions, recommend alternative technical and business approaches, and lead engineering efforts to meet ambitious timelines with optimal solutions.
- Produces architecture and design artifacts for complex applications while being accountable for 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.
- Contributes to the on-going development of the team, including recruitment, paired programming, peer review and mentoring.
- 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.
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Excellent understanding of Java 17+ fundamentals and frameworks (e.g. Spring Boot), OO programming paradigms, multi threading, messaging technologies, and computer networks. Experience of low-latency programming techniques and technologies (e.g. Inter-Process Communication, Memory-Mapped Files and Ring Buffers).
- Experience with gRPC and Google Protocol Buffers. Experience of working on distributed systems and detailed knowledge of distributed systems design patterns.
- Detailed knowledge of relational database technologies (e.g., PostgreSQL and CockroachDB), database resiliency, recoverability, scalability, and security.
- Experience with event-driven architecture and distributed messaging technologies (Kafka). Experience with diagramming software (e.g., Lucid, Draw.io, Visio) and technical writing.
- Passion for technical innovation and staying up to date with emerging technologies.
- Understanding of the full software development lifecycle and agile methodologies.
- 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
- Hands on experience another statically compiled language like Golang, Rust or C++.
- Experience with caching technologies, e.g. Apache Ignite, GridGain, Hazelcast or Redis.
- Experience with infrastructure as code software, e.g. Terraform
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