Job
Description
Job Summary: We are seeking a versatile QA & AIOps Engineer with a minimum of 5 years of hands-on experience in both AI Ops implementation and intelligent QA automation. The ideal candidate will bridge the gap between DevOps, QA, and AI engineering ensuring robust infrastructure, AI agent deployment, intelligent testing, and automated monitoring for scalable AI systems. This role requires strong technical depth in cloud-native deployment (AKS/EKS), CI/CD pipelines, and AI/ML toolchains. Key Responsibilities: AI Ops & DevOps Engineering: Deploy and manage AI agents in scalable production environments (Azure/AWS). Set up and maintain Kubernetes clusters (AKS/EKS), Docker containers, and Ingress Controllers (NGINX/Traefik). Integrate platforms like Azure AI Foundry or AWS Bedrock into production pipelines. Implement auto-scaling policies using tools like KEDA or Horizontal Pod Autoscaler. Promote use of Vector Databases (e.g., Pinecone, Weaviate) for AI workflows. Configure infrastructure security: secrets management, access controls, and network policies. Use Helm charts to manage complex deployments and upgrades of AI services. Troubleshoot production issues, identify bottlenecks, and ensure high availability of AI workloads. Collaborate with SREs, DevOps, and ML engineers to ensure seamless agent deployment, monitoring, and scaling. QA & Intelligent Testing: Design, develop, and execute test cases (manual and automated) for web, API, and backend systems. Implement AI-driven test strategies (e.g., test prioritization, risk-based testing). Create and maintain automated test scripts using tools like JUnit, PyTest, and JMeter. Integrate QA automation into CI/CD pipelines with anomaly detection and predictive analytics. Log and manage bugs; track issues to closure with development and DevOps teams. Use AI-powered QA tools such as Test.ai, Functionize for test optimization. Monitor AIOps dashboards to validate intelligent issue detection and auto-remediation flows. Required Skills & Qualifications: 5+ years of combined experience in QA automation, DevOps, or AI Ops roles. Hands-on expertise with Kubernetes (AKS/EKS), Docker, Helm, CI/CD pipelines. Experience integrating AI/ML tools such as Azure AI Foundry, AWS Bedrock. Knowledge of AI Ops platforms, observability tools, and monitoring dashboards. Proficient in Python, Bash, or JavaScript. Familiarity with Vector DBs, AI observability, and security practices. Strong collaboration skills with cross-functional DevOps, QA, and AI engineering teams.
