ML Ops Engineer

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience with cloud platforms, CI/CD, containerization, model deployment, monitoring, and ML lifecycle management.

Roles and Responsibilities

• Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining.
• Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration.
• Deploy and manage machine learning models across cloud and on-premise environments.
• Implement model versioning, experiment tracking, feature management, and model governance.
• Build scalable infrastructure using Docker, Kubernetes, and cloud services.
• Monitor model performance, data quality, system health, and production workloads.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
• Troubleshoot production ML systems and optimize reliability, scalability, and performance.
• Implement security, access controls, logging, and compliance best practices.

Required Skills

• 5+ years of experience in DevOps, ML Engineering, MLOps, or a related field.
• Strong experience with MLOps concepts and ML lifecycle management.
• Hands-on experience with Python and scripting.
• Experience with AWS, Azure, or GCP.
• Strong knowledge of Docker and Kubernetes.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar ML platforms.
• Experience with Git, Terraform, and infrastructure automation.
• Knowledge of model monitoring, observability, data validation, and model performance tracking.
• Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures.

Preferred Skills

• Experience with Apache Airflow, Databricks, Spark, or Kafka.
• Knowledge of LLMOps/GenAI deployment and monitoring.
• Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton.
• Familiarity with Prometheus, Grafana, ELK, or similar observability tools.
• Understanding of ML security, governance, and responsible AI practices.

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.