Sr Lead Software Engineer - Java, Python, AI/ML

JPMorgan Chase & Co.•Jersey City, NJ

1d•$175,750 - $260,000

About The Position

As a Senior Lead Software Engineer at JPMorganChase within the Commercial and Investment Bank's Engineering and Architecture Team, 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. You will be a part of the AI For Technology team that works closely with other tech teams across the line of business to apply AI technique to Software Engineering, applying AI to the trade and accelerating them through the journey. This is grounded in Software Engineering and applying AI solutions instead of building AI models. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Auto-GPT)
Advanced proficiency in Java or Python for software system development; strong grasp of software engineering best practices, system design, application development, testing, and operational stability
Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals and coding assistants
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in Java and/or Python programming languages
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Good understanding of data structures, algorithms, and practical machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn)
Hands-on practical experience delivering system design, application development, testing, and operational stability
Ability to tackle design and functionality problems independently with little to no oversight

Nice To Haves

Experience working at code level with advanced AI/ML business applications (e.g., LLMs, Generative AI, NLP)
AWS Certifications (Solution Architect Associate or Professional) are advantageous
In-depth knowledge of the financial services industry and their IT systems

Responsibilities

Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
Develops secure and high-quality production code, and reviews and debugs code written by others
Lead a local team of software engineers and applied AI/ML practitioners, driving accountability and engagement within a global organization.
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Architect, design, and develop secure, high-quality production software systems using Java or Python, integrating AI/ML techniques such as LLMs, Generative AI, and coding assistants
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Design experiments, implement algorithms, validate results, and productionize performant, scalable, and trustworthy AI/ML solutions.
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.
Drives decisions that influence the product design, application functionality, and technical operations and processes