ML OPS ENGINEER

About the role

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines.

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.

Requirements

• 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.

Qualifications

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