Senior Lead Software Engineer-AI Foundation Services

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

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 sensitive data
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
• Demonstrated ability to collaborate across product, application, infrastructure, and security teams to deliver complex technical outcomes

Preferred qualifications, capabilities, and skills

• Experience building or integrating GPU-backed model hosting, inference, training, or batch processing platforms
• Experience with LLM and model serving patterns, including routing, autoscaling, model gateways, inference optimization, evaluation workflows, and guardrail integration
• Experience optimizing AI/ML workloads for latency, throughput, reliability, and cost using techniques such as profiling, batching, caching, concurrency tuning, and capacity modeling
• Experience creating reusable developer enablement assets such as golden paths, reference architectures, deployment templates, onboarding playbooks, automated test harnesses, and operational runbooks