Senior QA Automation & MLOps Engineer

Job Summary

Lead quality assurance strategy, test automation, and validation for OmniOps’ AI inferencing and data science infrastructure. Ensure quality, reliability, security, and scalability of complex AI and MLOps platforms while mentoring QA engineers and driving best practices across engineering teams.

Responsibilities

• Develop and execute comprehensive QA strategies, test plans, automation frameworks, and quality standards for AI and cloud-native platforms.
• Design and implement end-to-end integration testing across the MLOps lifecycle, from data ingestion through live model inferencing.
• Develop scalable automated test frameworks using Playwright with Python for functional, regression, integration, API, and end-to-end testing.
• Design and implement automated security testing including credential leakage detection, dependency auditing, container image scanning, and vulnerability testing.
• Plan and execute performance, stress, and load testing using JMeter, Locust, or similar tools for AI inference endpoints and critical platform components.
• Integrate functional, integration, performance, and security testing into GitLab CI/CD pipelines and define automated QA gates.
• Use SQL to validate data integrity, perform backend verification, and troubleshoot issues across Linux, Docker, Kubernetes, and distributed cloud-native environments.
• Mentor mid-level QA and automation engineers while promoting QA best practices across the Engineering department.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or related technical field.
• 5+ years of experience in Software Quality Assurance, Test Automation, or related role.
• Expert-level proficiency in Playwright with Python.
• Strong experience with automated testing frameworks such as pytest or TestNG.
• Proven experience testing cloud-native, data-intensive, distributed, or highly integrated systems.
• Experience implementing security testing tools and practices including SAST, DAST, OWASP ZAP, SonarQube, or Trivy.
• Hands-on experience with JMeter, Locust, or similar performance testing tools.
• Advanced SQL skills, particularly with PostgreSQL, and strong understanding of REST APIs and backend architecture.
• Comfortable working in Linux, Docker, and Kubernetes environments with experience in GitLab CI/CD or similar platforms.
• Strong understanding of AI/ML and MLOps workflows, model training, deployment, and inferencing, with experience in tools such as JupyterHub, MLflow, or similar platforms.

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