MLOps Engineer
Job Description
Senior MLOps Engineer bridges machine learning and IT operations by deploying, monitoring, scaling, and maintaining ML models in production. They lead automation of ML pipelines, ensuring AI systems are reliable, reproducible, and efficiently integrated across enterprise infrastructure.
Key Responsibilities
- Architect and manage end-to-end machine learning production pipelines.
- Deploy, monitor, and maintain ML models across cloud environments.
- Build and optimize CI/CD pipelines specifically for ML workflows.
- Automate model training, retraining, versioning, and performance evaluation.
- Collaborate with data scientists to ensure models are production-ready.
- Monitor model drift, performance degradation, and trigger retraining pipelines.
- Establish data pipeline infrastructure for model training and validation.
- Mentor junior MLOps engineers and drive best engineering practices.
Skill & Experience
- Proficiency in Python and ML frameworks like TensorFlow and PyTorch.
- Hands-on experience with Docker, Kubernetes, and container orchestration.
- Strong knowledge of cloud platforms including AWS, GCP, and Azure.
- Experience with MLOps tools like MLflow, Kubeflow, and Apache Airflow.
- Familiarity with CI/CD tools including GitHub Actions and GitLab CI.
- Strong analytical thinking, communication, and cross-functional team collaboration.
Note: Salary depends on experience and skills and is paid in local currency.
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