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Software Engineer III – AI Research at JPMorgan Chase, crafting Python‑first AI/ML platforms and Generative AI apps on a global scale.
Lead secure, scalable production AI solutions, harness CI/CD, IaC, and AI‑assisted coding, collaborating with AIR and MLCoE.
Bring your engineering skills and Join JPMorgan Chase where you will build and ship production AI at global scale. As a Software Engineer III on JPMorgan Chase’s AI/ML Data Platforms — AI Research team, you’ll design Python‑first platforms and GenAI apps (including agents), turn research into secure, scalable products, and automate across cloud with modern CI/CD and IaC. Partner with AIR and MLCoE to shape how AI is built and deployed across the firm.
As a Software Engineer III at JPMorgan Chase within the AI/ML Data Platforms - AI Research team, you are an integral part of a team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will be responsible for delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. In this context, your primary clients will be AI Research (AIR) and the Machine learning Centre of Excellence (MLCoE)
Job responsibilities
- Develops and writes software applications for AI/ML platforms as well as building Generative AI based applications including Agents – particularly strong in Python.
- Utilizes creative problem-solving skills to design, develop, and troubleshoot technical solutions, thinking beyond conventional approaches to innovate and resolve complex technical challenges.
- Has an interest and familiarity with agent-based programing, especially with newest techniques
- Proactively identifies opportunities to streamline, eliminate, or automate the remediation of recurring issues and developer challenges, enhancing the operational efficiency and excellence of software applications and systems.
- Uses CI/CD to deliver public and private cloud platforms, as well as familiarity with experimentation environments (e.g. Jupyter Notebooks)
- Works closely with Data Scientists and AI Researchers to advance experiments into more robust, scalable, highly optimized production‑grade apps.
- Adds to team culture of opportunity, inclusion, and respect/
- 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 of applied experience
- Practical experience in Infrastructure as Code development, ideally using Terraform.
- Hands‑on practical experience in application development, testing, and ensuring operational stability.
- Advanced proficiency in one or more programming languages, with a strong focus on Python.
- Expertise automation and continuous integration, delivery, and testing (CI/CD/CT) methods.
- Comprehensive understanding of the Software Development Life Cycle (SDLC) and Model Development Life Cycle (MDLC).
- Deep understanding of agile methodologies and basic proficiency in architectural frameworks.
- Demonstrated proficiency in platform development and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, etc.).
- 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 – you can only have 4 bullets preferred
- Demonstrates initiative in learning and adapting to new technologies and methodologies, as well as demonstrable mastery of AI tools to enhance productivity and efficiency in daily tasks.
- Self‑motivated and proactive, with a strong ability to identify issues and challenge the status quo.as well as proven problem‑solving skills with a focus on innovation and continuous improvement.
- Experience in / exposure to a major business facing integrated application environment (e.g. risk, trading) and working with business facing developers
- Excellent communication and collaboration skills to work effectively within cross‑functional teams.