Compunnel, Inc.
Overview
We are seeking a highly skilled SDET with strong expertise in Python, AWS, and modern testing tools to support the development of a robust AI/ML data platform. This role focuses on building intelligent validation frameworks and infrastructure to ensure the reliability and performance of large-scale distributed systems. Key Responsibilities
Design and develop high-performance tools and services to validate ML data pipelines and AI infrastructure. Build platform-level test automation frameworks using Python, Terraform, and cloud-native practices. Integrate automated testing, resilience checks, and observability into CI/CD pipelines. Lead initiatives to embed testability and validation as code across the ML platform stack. Collaborate with engineering, MLOps, and infrastructure teams to ensure quality across platform components. Develop reusable components to support scalable and modular quality tooling. Mentor junior engineers and contribute to technical standards within 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 backend systems or platform engineering. Expert-level proficiency in Python and experience with testing frameworks such as Pytest and Playwright. Strong experience with AWS services including Lambda, S3, ECS/EKS, Step Functions, and CloudWatch. Proficiency in Infrastructure as Code using Terraform. Experience with CI/CD tools such as GitHub Actions, Jenkins, or similar. Solid understanding of software engineering best practices including code quality, performance optimization, and observability. Preferred Qualifications
Experience in test engineering for AI/ML platforms or data infrastructure. Familiarity with MLOps practices and tools.
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We are seeking a highly skilled SDET with strong expertise in Python, AWS, and modern testing tools to support the development of a robust AI/ML data platform. This role focuses on building intelligent validation frameworks and infrastructure to ensure the reliability and performance of large-scale distributed systems. Key Responsibilities
Design and develop high-performance tools and services to validate ML data pipelines and AI infrastructure. Build platform-level test automation frameworks using Python, Terraform, and cloud-native practices. Integrate automated testing, resilience checks, and observability into CI/CD pipelines. Lead initiatives to embed testability and validation as code across the ML platform stack. Collaborate with engineering, MLOps, and infrastructure teams to ensure quality across platform components. Develop reusable components to support scalable and modular quality tooling. Mentor junior engineers and contribute to technical standards within 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 backend systems or platform engineering. Expert-level proficiency in Python and experience with testing frameworks such as Pytest and Playwright. Strong experience with AWS services including Lambda, S3, ECS/EKS, Step Functions, and CloudWatch. Proficiency in Infrastructure as Code using Terraform. Experience with CI/CD tools such as GitHub Actions, Jenkins, or similar. Solid understanding of software engineering best practices including code quality, performance optimization, and observability. Preferred Qualifications
Experience in test engineering for AI/ML platforms or data infrastructure. Familiarity with MLOps practices and tools.
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