Software Engineer - AI/ML Data Platforms - AI Engineer - Python

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We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

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As a Lead Software Engineer at JPMorgan Chase within the AI/ML Data Platforms business, youare 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. 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. In this context, your primary clients will be AI Research (AIR) and the Machine learning Centre of Excellence (MLCoE)

Job responsibilities\n

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• Works closely with Data Scientists and AI Researchers to advance experiments into more robust, scalable, highly optimized production-grade apps.
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• Develops and writes software applications for AI/ML platforms as well as building Generative AI based applications including Agents.
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• Utilizes creative problem-solving skills to design, develop, and troubleshoot technical solutions, thinking beyond conventional approaches to innovate and resolve complex technical challenges.
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• 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.
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• Leads evaluation sessions (cross-team) to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
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• 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.
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• 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.
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• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
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• Mentors engineer within the team and drive practice across junior engineers in the team, as well as researchers / data scientists as they prototype solutions
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• Adds to team culture of diversity, opportunity, inclusion, and respect
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Required qualifications, capabilities, and skills\n

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• Formal training or certification on software engineering concepts with applied Full stack development experience.
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• Practical experience in Infrastructure as Code development, ideally using Terraform.
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• Hands‑on practical experience in system design, application development, testing, and ensuring operational stability.
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• Advanced proficiency in one or more programming languages, with a strong focus on Python.
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• Expertise in automation and continuous integration, delivery, and testing (CI/CD/CT) methods.
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• 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.
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• 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
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• Comprehensive understanding of the Software Development Life Cycle (SDLC) and Model Development Life Cycle (MDLC).
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• Deep understanding of agile methodologies and basic proficiency in architectural frameworks.
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• Demonstrated proficiency in platform development and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
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Preferred qualifications, capabilities, and skills\n

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• Demonstrable mastery of AI tools to enhance productivity and efficiency in daily tasks.
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• Self‑motivated and proactive, with a strong ability to identify issues and challenge the status quo.
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• Demonstrates initiative in learning and adapting to new technologies and methodologies.
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• Experience in / exposure to a major business facing integrated application environment (e.g. risk, trading) and working with business facing developers
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• Proven problem‑solving skills with a focus on innovation and continuous improvement.
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• Excellent communication and collaboration skills to work effectively within cross‑functional teams.
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