OmniOps is a Riyadh-based technology solutions provider, serving organizations across Saudi Arabia and beyond. With offices in Jordan, Egypt, and Morocco, we specialize in empowering businesses to migrate and scale their AI technology infrastructure confidently, achieving a high level of maturity in their digital landscapes. Our comprehensive services and products guarantee seamless cloud migration, scalability, and management, ensuring optimal performance and consistent reliability for our valued partners.

Job Purpose

The Senior QA Automation & MLOps Engineer will be responsible for leading the quality assurance strategy, test automation, integration testing, security validation, and performance testing for OmniOps AI inferencing and data science infrastructure.

The role will focus on ensuring the quality, reliability, security, and scalability of complex AI and MLOps platforms, including automated cluster provisioning, model training services, data pipelines, artifact management, and live inference environments.

The successful candidate will be a technically strong QA professional with hands-on expertise in automation, cloud-native systems, CI/CD, security testing, and AI/ML infrastructure. The role also includes mentoring engineers and driving QA best practices across OmniOps engineering teams.

Key Responsibilities

Lead the development and execution of comprehensive QA strategies, test plans, automation frameworks, and quality standards for OmniOps AI and cloud-native platforms.
Design and implement end-to-end integration testing across the MLOps lifecycle, from data ingestion and model training through artifact management and live model inferencing.
Develop scalable and reusable automated test frameworks using Playwright with Python.
Design and execute automated functional, regression, integration, API, and end-to-end testing.
Validate complex integrations between multiple services, APIs, cloud/on-premises clusters, allocation engines, workflows, and infrastructure components.
Design and implement automated security testing and quality gates, including credential leakage detection, dependency auditing, container image scanning, and vulnerability testing.
Validate security controls and isolation requirements within multi-tenant and distributed environments.
Plan and execute performance, stress, and load testing for AI inference endpoints, APIs, cluster allocation, and other critical platform components using JMeter, Locust, or similar tools.
Integrate functional, integration, performance, and security testing into GitLab CI/CD pipelines.
Define and optimize automated QA gates to support secure, reliable, and zero-downtime deployments.
Work closely with DevOps and engineering teams to identify and resolve quality issues throughout the development lifecycle.
Perform advanced API testing and validate backend services, data flows, and system integrations.
Use SQL to validate data integrity, perform backend verification, and support complex test scenarios.
Troubleshoot issues across Linux, Docker, Kubernetes, and distributed cloud-native environments.
Participate in the testing and validation of AI/ML workflows, including model training, model deployment, artifact management, and inference services.
Identify defects, analyze root causes, and work with engineering teams to ensure timely resolution.
Establish and continuously improve QA processes, automation coverage, technical documentation, and test reporting.
Define and monitor QA metrics and provide regular insights into quality, test coverage, defect trends, and platform risks.
Collaborate with Product Managers, Software Engineers, DevOps, Security teams, and Data Scientists to clarify requirements and ensure quality is considered throughout the product lifecycle.
Mentor and provide technical guidance to mid-level QA and automation engineers.
Promote QA best practices and a culture of quality ownership across the Engineering department.

Required Qualifications

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