Lead Software Engineer - Java / Python / AWS / AI/ML
JPMorgan Chase · Plano, United States · 16h ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
Our Consumer & Community Banking Group depends on innovators like you to serve consumers, small businesses, municipalities and non-profits. You’ll support the delivery of award winning tools and services that cover everything from personal and small business banking as well as lending, mortgages, credit cards, payments, auto finance and investment advice. This group is also focused on developing and delivering cutting edged mobile applications, digital experiences and next generation banking technology solutions to better serve our clients and customers.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Commercial & Community Banking, 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:
As a Lead Software Engineer at JPMorganChase within the Commercial & Community Banking, 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:
- Design and develop creative full-stack software solutions using innovative approaches.
- Build and implement AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
- 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.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
- Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
- Communicate project status clearly and manage priorities across multiple initiatives.
- Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
- Codes in Java, AWS ECS, EKS, and Postgres
- Utilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines
Required qualifications, capabilities, and skills:
- Formal training or certification in Software Engineering and 5+ years applied experience
- Strong system design, application development, and operational stability skills in production environments.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
- Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
- Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
- Familiarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
- Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
- Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
- Familiarity with DevOps practices and tools for continuous integration and deployment.
Preferred qualifications, capabilities, and skills:
- Cloud certification in AWS, GCP, or Azure.
- Working knowledge of Python (for AI/ML based implementations) a plus
- Experience with multi region service deployments and zero downtime deployment
- Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs
- Track record delivering scalable, reliable, and secure products from concept to launch.
- Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.