Lead Software Engineer - Backend Java, Markets Tech

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 Lead Software Engineer at JPMorgan Chase, 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 delivering critical technology solutions supporting the Equities Broker Dealer and Prime Finance platform, including securities lending, trade lifecycle, and clearing & settlement capabilities, in alignment with the firm’s business objectives. You will also integrate AI/ML and GenAI capabilities into core platforms in a way that is secure, governable, and operable at enterprise scale.

Job Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with the ability to solve complex problems and deliver scalable solutions across distributed systems and microservices architectures Develops secure, high-quality production code, and reviews and debugs code written by others to ensure engineering excellence Build and integrate AI/ML and GenAI into production platforms—LLMs, RAG, embeddings, vector databases, and MLOps for lifecycle management, monitoring, and governance.

Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability, resiliency, and performance of applications Drives architecture and design decisions influencing system scalability, performance, and long-term platform evolution Leads evaluation sessions with internal teams and stakeholders to assess architectural designs and ensure alignment with enterprise technology strategies Contributes to platform modernization through adoption of cloud-native, event-driven, and microservices-based architectures Partners with business, product, and operations teams to translate Prime Finance and Broker Dealer requirements into scalable technical solutions Ensures robust delivery across environments with strong focus on SDLC, testing (unit, SIT, UAT), and production stability 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.

Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years of applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability in large-scale enterprise environments Advanced proficiency in Java, Spring Boot and REST APIs programming language within microservices in a distributed systems architecture Strong experience with.

Relational databases (Oracle/DB2) and/or NoSQL (MongoDB) and CI/CD pipelines, DevOps, and automation practices Proficient in all aspects of the Software Development Life Cycle (SDLC) Strong problem-solving, analytical, and stakeholder communication skills Demonstrated proficiency in building high-volume, low-latency, mission-critical financial systems Hands-on experience integrating AI/ML and GenAI into enterprise applications, including LLMs, RAG pipelines, embeddings, vector stores/databases, model evaluation, and production monitoring (MLOps).

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 Practical experience working across cross-functional teams in complex enterprise organizations Preferred qualifications, skills, and capabilities Cloud-native development (AWS or equivalent) Exposure to Prime Finance, Securities Lending, or Broker Dealer platforms Experience with event-driven architectures (Kafka, MQ) and messaging systems Familiarity with modern front-end technologies (React, UI frameworks) Exposure to domain-driven design, full-stack development, and modern architectural patterns Experience with observability tools (Splunk, Grafana, Prometheus, ELK) Interest in adopting emerging technologies (AI-assisted development, automation, advanced analytics)