Senior Lead AI/ML Platform Engineer

JPMorgan Chase; Wilmington (DE); On-site; USD 152,000 - 225,000; Full time

Job summary

JPMorgan Chase is hiring a Senior Lead Software Engineer to design, build, and operate foundational cloud infrastructure for data scientists and ML engineers. You will lead platform reliability, scalability, and automation while collaborating across teams to solve complex infrastructure challenges.

Expect exposure to enterprise-grade AI/ML pipelines and GPU workloads. The role emphasizes responsible AI practices, secure coding, and scalable SDLC tooling to accelerate experimentation and

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Experience delivering secure, production-quality code in Python or Java.
• Strong foundations in distributed systems, microservices, and platform architecture/design principles.
• Proven ability to architect and operate cloud-native infrastructure on AWS (compute, networking, storage, security) and other major clouds.
• Demonstrated expertise with infrastructure-as-code tooling, specifically Terraform, in large-scale cloud environments.
• Hands‑on experience with Docker and Kubernetes, including AWS EKS operations.
• Experience building or supporting production AI/ML platforms (training, deployment, and model serving/inference), including GPU infrastructure/tooling.
• Strong DevOps/platform engineering practices: CI/CD, release automation, automated testing, and observability (monitoring/logging/tracing).
• Experience with SQL/NoSQL databases and data integration; strong Linux, scripting, and networking fundamentals.
• 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.

Responsibilities

• Builds and maintains reusable AI/ML platform infrastructure and shared services to support development, deployment, and operations at scale.
• Architects, deploys, and operates secure cloud and container-based environments for training and inference, including GPU-intensive workloads.
• Design and implement platform tooling, automation, and infrastructure-as-code solutions to streamline model deployment, environment provisioning, release management, and operational support.
• Develops and maintains production-grade services, APIs, SDK integrations, and workflows that support model training, serving, evaluation pipelines, and AI application lifecycle management.
• Partners with data science, ML engineering, and application teams to translate model and compute requirements into platform standards and deployment patterns.
• Optimizes platform reliability, scalability, latency, and cost through orchestration, scheduling, and hardware acceleration.
• Establishes operational best practices including monitoring, logging, observability, access controls, incident response, and production troubleshooting.
• Supports enterprise LLM operationalization, including fine-tuning workflows, inference optimization, and evaluation; contribute to documentation and engineering standards.
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes, while establishing measurable validation standards and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• 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.

Skills

Python

Java

Distributed systems

Microservices

Platform architecture

AWS

Terraform

Docker

Kubernetes

CI/CD

Tools

AWS EKS

GPU infrastructure

Job description

JPMorgan Chase is hiring a Senior Lead Software Engineer to design, build, and operate foundational cloud infrastructure for data scientists and ML engineers. You will lead platform reliability, scalability, and automation while collaborating across teams to solve complex infrastructure challenges.

Expect exposure to enterprise-grade AI/ML pipelines and GPU workloads. The role emphasizes responsible AI practices, secure coding, and scalable SDLC tooling to accelerate experimentation and