Senior Lead Software Engineer-AI Foundation Services

Join JPMorganChase’s Chief Data & Analytics (AIML Data Platforms) team in Jersey City as a Senior Lead Software Engineer building AI foundation services for GenAI and ML at enterprise scale. You’ll lead hands-on delivery of secure, reliable, cloud-native platform capabilities (Kubernetes/CI/CD/IaC) and partner with application teams to create reusable integrations, reference implementations, and onboarding assets.

As a Senior Lead Software Engineer at JPMorganChase within the AIML Data Platforms – Chief Data and Analytics 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. In this role you will get to drive significant business impact through your capabilities and contributions and apply your deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

• Designs, builds, integrates, and optimizes AI Foundation Services infrastructure components for GenAI and traditional AI/ML platforms, with a focus on production-quality delivery and hands-on engineering execution
• Partners with Lines of Business (LOB) application teams to co-develop reusable AI/ML foundational service capabilities, managed service integrations, and platform adoption patterns
• Translates Line of Business (LOB) application requirements into clear technical designs, implementation plans, and engineering deliverables that support successful launch and early operational readiness
• Helps de-risk AI/ML platform delivery across performance, scale, reliability, and security by contributing to non-functional requirements, test plans, runbooks, observability, and production readiness reviews
• Builds reusable engineering assets such as reference implementations, deployment templates, test harnesses, onboarding guides, and GPU/training/serving baselines for model hosting platforms
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing).
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
• Implements durable, maintainable code solutions and production platform capabilities that can be reused by multiple application teams and extended by other engineers
• Collaborates with product, platform, security, infrastructure, and application teams to resolve complex technical issues and deliver AI Foundation Services capabilities aligned to business priorities
• Participates in technical design reviews, operational readiness reviews, incident analysis, and continuous improvement activities to improve service reliability, scalability, and developer experience

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience
• Hands-on experience designing, building, testing, and operating production software systems, distributed services, or platform capabilities
• Practical experience with AI/ML platform capabilities, model serving, model hosting, data access patterns, platform integrations, or infrastructure services supporting AI/ML workloads
• Experience developing cloud-native applications or platform services using Kubernetes, containers, CI/CD, infrastructure-as-code, and modern engineering practices
• Proficiency in one or more programming languages such as Python, Java, Go, or similar, with demonstrated ability to deliver high-quality production code
• Experience translating business or application team requirements into technical designs, implementation tasks, delivery milestones, and operational support plans
• Working knowledge of performance engineering and production reliability practices, including load testing, capacity planning, monitoring, alerting, SLOs/SLIs, incident response, and root-cause analysis
• Experience applying secure-by-design engineering practices, including access controls, secrets management, vulnerability remediation, and secure handling of s…