Job Description

About the Role We are seeking a highly skilled MLOps Engineer to design, implement, and manage the infrastructure, deployment, and monitoring pipelines for machine learning systems. You will ensure the scalability, reliability, and performance of AI workflows while enabling smooth collaboration between data science and engineering teams. Key Responsibilities Infrastructure & Deployment : Implement automated ML deployment pipelines with tools such as Kubernetes, Docker, MLflow, Kubeflow, and Azure ML. CI/CD Pipelines : Build and maintain CI/CD systems using Jenkins, GitHub Actions, or GitLab CI. Performance Monitoring : Monitor model performance, latency, and drift; implement alerts and dashboards. Scalability & Reliability : Design infrastructure that supports dev, staging, and production environments. Observability : Deploy observability tools (Prometheus, Grafana, ELK Stack, Azure Monitor) for real-time system insights. Vector Database Infrastructure : Manage environments for Pinecone, Weaviate, or Milvus to support AI-driven applications. Requirements Required Qualifications 3+ years in DevOps/Infrastructure and 2+ years in MLOps . Experience with ML lifecycle tools : MLflow, Kubeflow, Azure ML. Strong knowledge of CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, Azure DevOps). Proficiency in Python, Bash/Shell scripting, and YAML . Expertise in Docker, Kubernetes, Helm . Hands-on with Infrastructure as Code (Terraform, Ansible, CloudFormation) . Cloud expertise (AWS, GCP, or Azure?Azure ML preferred). Familiarity with monitoring stacks (Prometheus, Grafana, ELK).