Lead Software Engineer - Full Stack/generative Ai/llm

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

Lead Software Engineer focused on integrating and driving adoption of AI-assisted engineering practices, LLMs, and generative AI within a financial services context. The role involves developing secure, high-quality code, troubleshooting technical problems, and coaching engineers on responsible AI use, while ensuring adherence to security and compliance standards.

What you'd actually do

  1. 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.
  2. 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.
  3. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  4. Develops secure high-quality production code, and reviews and debugs code written by others
  5. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • 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
  • Advanced proficiency in programming languages (Java, Python, C#, etc.)
  • Hands-on work with Large Language Models (LLMs) and generative AI.
  • Familiarity with AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face).
  • Experience with distributed systems and cloud platforms (AWS, GCP, Azure).
  • Expertise in microservices, RESTful APIs, and database technologies (relational/NoSQL).
  • Familiarity with containerization tools (Docker, Kubernetes, Helm).
  • Experience with performance testing tools (JMeter, Blazemeter) and Chaos Monkey Testing.
  • Skilled in development and testing tools and frameworks (JUnit, UDF, Tophat, Cucumber, Groovy, Postman, REST Assured, Eclipse, Maven, Jenkins, IntelliJ).

Nice to have

  • Cloud certification (AWS, GCP, Azure).
  • Practical cloud-native development experience.
  • In-depth knowledge of the financial services industry and their IT systems.
  • Experience in creating and executing performance and chaos test scripts.
  • Experience in or understanding of A/B Testing, Chaos Monkey Testing, Engineering principles.
  • Effective communication across teams and management, with a proactive approach to process improvement.
  • Strong system design, application development, and operational stability skills.

What the JD emphasized

  • 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

Other signals

  • AI-assisted engineering practices
  • LLMs and generative AI
  • responsible AI use