QA Automation Engineer AI Applications

You will join a forward-thinking engineering environment focused on ensuring the quality and reliability of next-generation AI-powered products used in real-world, high-impact applications. The role combines advanced test automation engineering with hands-on evaluation of AI/ML systems, requiring both technical depth and product intuition. You will design and scale robust QA frameworks that support complex AI services across APIs, models, and user-facing features. The position sits at the intersection of software quality engineering and artificial intelligence, where testing goes beyond traditional validation into model behavior and output evaluation. You will work closely with engineers, data scientists, and product teams in an Agile, collaborative setup. A strong emphasis is placed on automation, scalability, and continuous improvement of testing systems. This is a high-ownership role where your work directly influences product reliability and user trust in AI systems. Accountabilities: Design, build, and maintain automated test suites, evaluation scenarios, datasets, and metrics for AI-powered features and systems. Develop scalable QA automation frameworks and test harnesses for APIs, backend services, and AI applications using Python. Automate QA workflows within CI/CD pipelines (e.g., GitHub Actions, Jenkins), integrating AI-assisted testing tools to improve coverage and efficiency. Define QA strategy, test plans, and acceptance criteria across functional, integration, API, and AI/ML-specific testing domains. Evaluate AI/ML model outputs for correctness, consistency, bias, and expected product behavior using structured rubrics and measurable quality gates. Build and maintain automated regression and validation pipelines embedded in deployment workflows. Participate in cross-team engineering initiatives, code reviews, and ensure QA best practices are consistently applied. Document testing strategies, findings, and recommendations, while contributing to knowledge sharing across teams. Mentor peers and help elevate QA maturity across engineering teams working on AI-driven systems.

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. 5+ years of experience in software QA, test automation, or software engineering roles. Strong programming skills in Python with hands-on experience building test automation frameworks. Experience working with CI/CD systems and modern development workflows. Practical use of AI-powered tools or coding assistants to enhance testing productivity and automation quality. Solid understanding of software architecture, design patterns, and scalable system design. Experience designing QA frameworks or platforms used across multiple teams or services. Strong analytical thinking, problem-solving ability, and attention to detail. Excellent communication and collaboration skills in Agile, distributed environments. Nice to have: experience testing AI/ML systems, validating models, or working with tools such as PyTorch or TensorFlow. Nice to have: familiarity with MLOps practices, data pipelines, or tools such as Apache Airflow, MLflow, or cloud platforms (AWS, Azure, GCP).

Competitive compensation package aligned with experience and location, including potential bonuses and equity components. Flexible working arrangements supporting work-life balance. Comprehensive health and medical benefits depending on eligibility and location. Annual paid leave and additional wellness or personal days in addition to standard vacation entitlement. Budget for learning and development, including courses, certifications, and conferences. Access to cutting-edge AI projects and modern engineering toolchains. Opportunities to work in a global, cross-functional, and highly collaborative engineering environment. Strong culture of learning, experimentation, and technical growth. How Jobgether works