Building an enterprise-grade AI/ML Data Platform that enables scalable, secure, and responsible machine learning across J.P. Morgan Chase
Requirements
- Expert in Python with a strong understanding of object-oriented programming, testing frameworks, and automation libraries
- Experience building or validating platform infrastructure, with hands-on knowledge of CI/CD systems, GitHub Actions, Jenkins, or similar tools
- Solid experience with AWS services (Lambda, S3, ECS/EKS, Step Functions, CloudWatch)
- Proficient in Infrastructure as Code using Terraform to manage and provision cloud infrastructure
- Strong understanding of software engineering best practices: code quality, reliability, performance optimization, and observability
Responsibilities
- Design and build high-performance tools and services to validate the reliability, performance, and correctness of ML data pipelines and AI infrastructure
- Develop platform-level test solutions and automation frameworks using Python, Terraform, and modern cloud-native practices
- Contribute to the platform’s CI/CD pipeline by integrating automated testing, resilience checks, and observability hooks at every stage
- Lead initiatives that drive testability, platform resilience, and validation as code across all layers of the ML platform stack
- Collaborate with engineering, MLOps, and infrastructure teams to embed quality engineering deeply into platform components
- Build reusable components that support scalability, modularity, and self-service quality tooling
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field
- 5+ years of hands-on software development experience, including large-scale backend systems or platform engineering
- Mentor junior engineers and influence technical standards across the Test Engineering Program
