MLOps Engineer: Scalable ML Pipelines & Infra

We are seeking an experienced and highly motivated MLOps Engineer to join the Data and AI team. In this role, you will bridge the gap between machine learning development and scalable production systems by building, automating, and managing end-to-end ML pipelines. The ideal candidate will have strong expertise in cloud platforms, CI/CD automation, infrastructure-as-code, and product ionizing machine learning models in enterprise environments.

• Design, build, and maintain scalable ML infrastructure and CI/CD pipelines for training, testing, deploying, and monitoring machine learning models
• Automate model versioning, deployment, rollback strategies, and environment management across staging and production
• Collaborate closely with Data Scientists and Machine Learning Engineers to product ionize ML models and optimize deployment workflows
• Apply Infrastructure-as-Code (IaC) practices to provision and manage cloud-based ML infrastructure
• Implement monitoring, logging, and alerting solutions for ML systems, including model drift and data anomaly detection
• Optimize the performance, scalability, and reliability of model training and inference systems
• Ensure ML operations adhere to organizational security, compliance, and reliability standards
• Maintain comprehensive documentation for systems, workflows, processes, and operational procedures
• Support continuous improvement initiatives related to MLOps, Dev Ops, and AI/ML operational practices

• Bachelor’s Degree in Computer Science, Engineering, or a related field
• 3+ years of experience in MLOps, Dev Ops, or Machine Learning Engineering
• Hands‑on experience with Azure Dev Ops and Azure Machine Learning (Azure
  
  ML)
• Proficiency with cloud platforms such as AWS, Azure, or GCP
• Experience with containerization and orchestration technologies including Docker and Kubernetes
• Strong programming skills in Python, Bash, and Power Shell
• Experience working with REST APIs
• Experience with Infrastructure-as-Code tools such as Terraform or ARM templates
• Familiarity with CI/CD tools including Jenkins, Git Hub Actions, or Azure Dev Ops Pipelines
• Hands‑on experience with machine learning frameworks such as Tensor Flow, PyTorch, and Scikit‑learn
• Familiarity with ML tools such as MLflow, TFX, DVC, or Kubeflow
• Experience with workflow orchestration tools such as Apache Airflow or Prefect
• Strong troubleshooting, analytical, and problem‑solving skills
• Excellent verbal and written communication skills

• Experience with monitoring and logging tools such as Azure Monitor, Prometheus, or Grafana
• Experience working in Agile development environments
• Experience collaborating with Data Science and Machine Learning teams
• Strong collaboration skills with experience working in cross‑functional technical teams
• Experience optimizing scalable AI/ML operations and infrastructure

• Certified Kubernetes Administrator (CKA) or equivalent certification