As a Lead Software Engineer at JPMorgan Chase within the AI/ML Data Platform team, you will play a pivotal role in promoting the Test Engineering Program. This strategic initiative focuses on building robust, intelligent validation frameworks and infrastructure that power our ML and data products. You will architect and develop tools, services, and automation that elevate platform assurance across large-scale distributed systems, collaborating with engineering, MLOps, and infrastructure teams to embed quality engineering deeply into platform components.
Job 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.
Required qualifications, capabilities, and skills
•Formal training or certification on Computer Science concepts and proficient advanced experience.
•8+ years of hands-on software development experience, including large-scale backend systems or platform engineering.
•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.
Preferred qualifications, capabilities and skills
•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).
•Familiarity with banking or financial domain use cases, including data governance and compliance-focused development.
•Knowledge of platform security, monitoring, and resilient architecture patterns.
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