We are seeking a skilled MLOps Engineer with 2+ Years of experience to design, deploy, and maintain machine learning workflows, ensuring scalability, reliability, and efficiency. The role involves integrating ML models into production, automating pipelines, monitoring performance, and enabling CI/CD practices for machine learning systems. Responsibilities: Develop and manage end-to-end ML pipelines. Automate model deployment and retraining processes. Monitor and optimize model performance and infrastructure. Collaborate with data scientists, engineers, and DevOps teams. Implement CI/CD pipelines for ML workflows. Ensure compliance with data and model governance. Skills Required: Proficiency in Python, cloud platforms (AWS, Azure, GCP), and containerization (Docker, Kubernetes). Experience with ML tools like TensorFlow, PyTorch, MLflow, or Kubeflow. Strong understanding of CI/CD, version control, and DevOps practices. Knowledge of data engineering and software development principles. This role is ideal for candidates passionate about bridging the gap between machine learning and scalable, production-grade systems.
Job Title
ML Ops Engineer