Lead Software Engineer - Backend Java, Markets Tech

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Commercial & Investment Bank

Lead Software Engineer responsible for integrating AI/ML and GenAI capabilities into core financial platforms, including LLMs, RAG, embeddings, vector databases, and MLOps. The role also involves driving the adoption of AI-assisted engineering practices and ensuring secure, governable, and operable solutions at an enterprise scale.

What you'd actually do

  1. 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
  2. Develops secure, high-quality production code, and reviews and debugs code written by others to ensure engineering excellence
  3. Build and integrate AI/ML and GenAI into production platforms—LLMs, RAG, embeddings, vector databases, and MLOps for lifecycle management, monitoring, and governance.
  4. Drive adoption of enterprise-authorized AI-assisted engineering practices (code review/refactoring, test acceleration, incident/root-cause analysis) with consistent validation standards (secure coding, peer review, automated testing) and reuse of effective patterns.
  5. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability, resiliency, and performance of applications

Skills

Required

  • 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 (coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Practical experience working across cross-functional teams in complex enterprise organizations

Nice to have

  • 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)

What the JD emphasized

  • integrating AI/ML and GenAI into core platforms
  • Build and integrate AI/ML and GenAI into production platforms
  • Drive adoption of enterprise-authorized AI-assisted engineering practices
  • Hands-on experience integrating AI/ML and GenAI into enterprise applications
  • Demonstrated experience leading effective use of approved AI-assisted software development tools

Other signals

  • integrating AI/ML and GenAI into core platforms
  • Build and integrate AI/ML and GenAI into production platforms
  • Drive adoption of enterprise-authorized AI-assisted engineering practices
  • Hands-on experience integrating AI/ML and GenAI into enterprise applications