Lead Software Engineer - Python, Full Stack / AI

JPMorgan Chase & Co.•Jersey City, NJ

14h•$156,750 - $215,000

About The Position

As a Lead Software Engineer - Python, Full Stack / AI at JPMorgan Chase in Commercial and Investment Bank's Treasury Technology, you will lead the build out of an end-to-end data platform to manage Capital, Liquidity, Balance sheet, Funding and billing (Transfer pricing). You will develop and enhance applications critical to the business, working closely with Treasury, Finance, Trading, Sales, and Quantitative Research teams. Your expertise in Python and agentic development will drive automation and innovation. You will contribute to a global platform, ensuring stability and delivering high-quality solutions. This role offers the chance to make a meaningful impact in a fast-paced, collaborative environment.

Requirements

Formal training or certification on software engineering concepts and 5+ years of applied experience
Strong Python skills and familiarity with agentic development (ADLC)
Demonstrated Engineering leadership, architecture and stakeholder management skills
Database design and modeling on modern data platforms (Databricks, snowflake)
Hands on experience in co-pilot or Claude-code to design and deliver end-to-end applications.
Working understanding of multi public cloud; exposure to AWS Cloud.
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Experience in Risk and Pnl in Markets
Understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
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

Nice To Haves

Experience in Risk and Pnl in Markets
Knowledge of Financial Markets and Products (Fixed Income, Derivatives) and Treasury concepts

Responsibilities

Full Stack development of UI Dashboard in React/Typescript and servers in the python stack.
Deliver data pipelines and ETL on data platforms such as Databricks and Snowflake
Executes software solutions, design, development, and technical troubleshooting
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.
Lead the use of LLMs for structured requirements analysis, including translating business and regulatory requirements into clear technical specifications.
Establish best practices for prompt-driven design and development, treating prompts as versioned, reviewable engineering artifacts.
Ensure prompt strategies support determinism, reproducibility, and traceability in regulated environments.
Ensure LLM-driven systems meet enterprise reliability and resilience expectations.
The role supervises how LLM-assisted and agent-driven development is performed, ensuring that model strengths, limitations, and risk profiles are understood, documented, and appropriately applied across different classes of software work
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.