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This listing doesn't post a salary. Most similar roles pay
$154,600–$200,312.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
The Senior Lead Software Engineer-AI Foundation Services joins the AIML Data Platforms team within Chief Data and Analytics to build AI foundation services for GenAI and ML at enterprise scale. You will lead the hands-on delivery of secure, cloud-native platform capabilities, including Kubernetes, CI/CD, and Infrastructure-as-Code. Daily responsibilities involve designing and optimizing infrastructure components, creating reusable engineering assets like reference implementations and deployment templates, and partnering with application teams to translate business requirements into technical designs. You will utilize Python, Java, or Go to develop durable code while managing model serving, hosting, and data access patterns. The role focuses on de-risking AI/ML platform delivery by addressing performance, scale, reliability, and security requirements while driving the adoption of AI-assisted engineering practices and responsible AI usage.
Design, build, and optimize AI Foundation Services infrastructure for GenAI and traditional AI/ML platforms.
Translate business requirements into technical designs, implementation plans, and engineering deliverables.
Develop reusable engineering assets including reference implementations, deployment templates, and onboarding guides.
Build cloud-native platform capabilities using Kubernetes, CI/CD, and Infrastructure as Code.
Implement durable, maintainable code solutions for production platform capabilities that support multiple application teams.
Drive adoption of AI-assisted engineering practices to improve code quality and delivery speed.
Manage non-functional requirements including performance, scale, reliability, security, and observability.
Participate in technical design reviews, incident analysis, and operational readiness reviews.
What we're looking for
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, or infrastructure services supporting AI/ML workloads.
Experience developing cloud-native applications using Kubernetes, containers, CI/CD, and infrastructure-as-code.
Proficiency in one or more programming languages such as Python, Java, or Go to deliver high-quality production code.
Experience translating business requirements into technical designs, implementation tasks, delivery milestones, and operational support plans.
Working knowledge of performance engineering, production reliability practices, secure-by-design engineering, and responsible AI use in engineering workflows.
Experience building GPU-backed model hosting platforms, LLM serving patterns, or creating reusable developer enablement assets (preferred).