Software Engineer III - Python / AI
Summary
Build Python-based data pipelines and React dashboards while integrating enterprise-approved LLMs into regulated financial workflows, ensuring security, auditability, and responsible AI practices.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III - Python/ AI at JPMorganChase within the Commercial and Investment Bank , you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
As a Software Engineer III - Python/ AI at JPMorganChase within the Commercial and Investment Bank , you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Build and maintain UI dashboards using React/TypeScript and backend services in the Python stack
- Design and deliver data pipelines/ETL on modern data platforms (e.g., Databricks, Snowflake)
- Execute software design, development, testing, and technical troubleshooting across the SDLC
- Ensure large language models (LLMs) are used as controlled, well-understood components of the engineering lifecycle (enterprise-approved models)
- Lead structured requirements analysis using LLM-assisted workflows to translate business and regulatory needs 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, traceability, and auditability in regulated environments
- Ensure LLM-driven systems meet enterprise reliability, resilience, and security expectations
- Coach teams on safe, compliant LLM/agent usage by documenting strengths, limitations, and risk profiles for different classes of engineering work
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience.
- Strong Python skills, including familiarity with agentic development practices
- Experience with database design and data modeling on modern data platforms (e.g., Databricks, Snowflake)
- Hands-on experience using approved AI-assisted development tools (e.g., copilots/LLM coding assistants) to design and deliver end-to-end applications, with strong validation habits for correctness, performance, and security
- Experience developing, debugging, and maintaining code in a large enterprise environment using one or more modern programming languages and database querying languages
- Strong understanding of SDLC and agile delivery practices, including CI/CD, application resiliency, and security controls
- Strong understanding of responsible AI use in engineering workflows (data sensitivity, secure handling of inputs/outputs, resiliency/security expectations), including experience coaching engineers on compliant adoption
- 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.
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end technologies
- Experience in Risk and Pnl in Markets
- Exposure to public cloud, with preference for AWS
- Knowledge of Financial Markets and Products (Fixed Income, Derivatives) and Treasury concepts)