J.P. Morgan Chase is building an enterprise-grade AI/ML Data Platform and needs to drive a Test Engineering Program to build robust, intelligent validation frameworks and infrastructure for ML and data products.
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.
- Exposure to machine learning workflows, model lifecycle management, or data engineering platforms.
- Experience with distributed systems, event-driven architectures (e.g., Kafka), and big data platforms (e.g., Spark, Databricks).
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.
- Mentor junior engineers and influence technical standards across the Test Engineering Program.
Other
- 5+ years of hands-on software development experience, including large-scale backend systems or platform engineering.
- Familiarity with banking or financial domain use cases, including data governance and compliance-focused development.
- Knowledge of platform security, monitoring, and resilient architecture patterns.
- Help shape the test engineering strategy for one of the largest AI/ML data platforms in the financial industry.
- Collaborate with highly skilled engineers and data scientists in a high-impact, growth-oriented environment.
