MLOps Engineer

Key Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.

• Automate ML workflows using CI/CD best practices.

• Deploy and manage machine learning models in production environments.

• Develop scalable data and model pipelines on cloud platforms.

• Monitor model performance, data drift, and system health.

• Collaborate with data scientists to productionize ML models.

• Implement model versioning, experiment tracking, and artifact management.

• Optimize infrastructure for performance, scalability, and cost efficiency.

• Ensure security, governance, and compliance for ML platforms.

• Troubleshoot production issues and improve operational reliability.

Required Skills

• 5+ years of experience in DevOps, Data Engineering, or MLOps.

• Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.

• Experience with containerization technologies like Docker and Kubernetes.

• Strong knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.

• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).

• Experience with Infrastructure as Code tools such as Terraform or CloudFormation.

• Knowledge of model monitoring, logging, and observability tools.

• Strong understanding of Git version control and software development best practices.

• Experience with Linux environments and shell scripting.

Preferred Qualifications

• Experience with Generative AI, LLM deployment, or RAG-based applications.

• Familiarity with Apache Airflow, Kafka, or Spark.

• Knowledge of feature stores and model registries.