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 withRelational 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)