Full Stack Lead Software Engineer- Python

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 Data Management, Data Tech 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. 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

• 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

• Lead with an AI-driven approach in daily tasks

• Spearhead the design and development of real-time, mission-critical using Python

• Tackle large-scale engineering challenges with technologies like Python Stack

• Innovate, troubleshoot, and optimize for performance and stability

• Inspire and foster a culture of creativity, inclusion, and technical excellence

• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

• Adds to team culture of diversity, opportunity, inclusion, and respect

• 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

• 5+ years of relevant software engineering experience

• Hands-on practical experience delivering system design, application development, testing, and operational stability

• This role ensures that large language models (LLMs) including models such as Claude, ChatGPT, and comparable enterprise-approved models - are used as controlled, well-understood components of the software engineering lifecycle.

• Proficiency in automation and continuous delivery methods

• Proficient in all aspects of the Software Development Life Cycle

• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

• In-depth knowledge of the financial services industry and their IT systems

• Practical cloud native 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

Preferred Qualifications, Capabilities, and Skills:

• Expertise in GenAI usage, transforming Software Engineering with an AI-first approach

• Advanced proficiency in Python software engineering skills

• Proficiency in Typescript, RAG, Vector, Graph, data structures, AWS and performance tuning

• Advanced understanding of CI/CD, application resiliency, and security for AI applications.

• Proficiency in testing and debugging low latency applications