Key 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

• Bachelor’s or master’s degree in computer science, Engineering, or a related technical field.

• 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

• 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.