MLOps Engineer

As an MLOps Engineer, your role will involve designing, implementing, and maintaining scalable machine learning pipelines and infrastructure. You should have a strong understanding of ML lifecycle management, CI/CD for ML models, and cloud platforms to ensure seamless integration between data science and production environments. Key Responsibilities: - Build, deploy, and manage end-to-end ML pipelines from experimentation to production. - Develop and maintain CI/CD workflows for ML models. - Automate training, testing, monitoring, and retraining processes. - Manage model versioning, deployment, and scaling in production environments. - Ensure observability, logging, and monitoring for ML models and pipelines. - Collaborate with data scientists, data engineers, and software engineers to deliver robust ML solutions. - Optimize cost, performance, and reliability of ML infrastructure. Required Skills: - Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). - Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, GitLab CI/CD, Argo). - Proficiency in Docker, Kubernetes, and container orchestration. - Experience with cloud platforms (AWS Sagemaker, Azure ML, GCP Vertex AI). - Knowledge of ML model monitoring, logging, and A/B testing. - Understanding of data pipelines, ETL, and feature stores. - Strong problem-solving and debugging skills. Good to Have: - Experience with infrastructure as code (IaC) using Terraform/CloudFormation. - Exposure to data versioning tools like DVC, MLflow, or Kubeflow. - Knowledge of security and compliance in ML systems.,