MLOps Engineer

Requirements: · Education: Bachelor’s Degree in Computer Science, Engineering, or a related field. · Experience: 3+ years of experience in MLOps, DevOps, or ML Engineering. · Azure DevOps and AzureML experience. Technical Expertise: · Proficiency in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). · Strong proficiency in Python, Bash, Powershell and experience with REST APIs · Experience with infrastructure as code (Terraform, ARM). Tool Proficiency: · Familiarity with CI/CD tools (Jenkins, GitHub Actions, ADO Pipelines) · Hands-on experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn · Familiarity with ML tools like MLflow, TFX, DVC, or Kubeflow · Experience with workflow orchestration (e.g., Apache Airflow, Prefect) Position Summary We are seeking an experienced and highly motivated MLOps Engineer to join our dynamic Data and AI team. In this role, you’ll bridge the gap between machine learning development and scalable production systems. You will be responsible for building, automating, and managing end-to-end ML pipelines that enable reliable and repeatable deployment of models across environments. Key Responsibilities · Develop and Implement CI/CD Pipelines: Design, build, and maintain scalable ML infrastructure and pipelines (CI/CD) for training, testing, deploying, and monitoring models. · Automation and Orchestration: Automate model versioning, deployment, and rollback strategies across staging and production. · Collaboration: Collaborate closely with Data Scientists and Machine Learning Engineers to productionize ML models. · Deploy Infrastructure Operations: Apply Infrastructure as Code (IaC) to provision and manage ML infrastructure in the cloud. · Monitoring and Troubleshooting: Implement observability for ML systems including monitoring, logging, and alerting of model drift and data anomalies. Optimize performance and scalability of model training and inference systems. · Security and Compliance: Ensure security, compliance, and reliability of ML operations across cloud platforms. · Documentation: Maintain comprehensive documentation of systems, processes, and workflows to facilitate knowledge sharing and collaboration.