Enabling Areas | IT | Assistant Manager | Data Scientist + ML Ops

Job Title -ML Ops EngineerKey Responsibilities:Design, implement, and maintain end-to-end ML pipelines for deploying and monitoring models in production.Collaborate with data scientists to transition models from development to production, ensuring reproducibility and efficiency.Automate processes for model training, validation, and deployment using CI/CD practices.Monitor model performance and implement retraining strategies as needed.Manage cloud infrastructure and resources for machine learning workloads, ensuring cost efficiency and scalability.Develop and maintain documentation for ML processes, architectures, and best practices.Troubleshoot and resolve issues related to model deployment and operational performance.Stay informed about the latest trends and advancements in ML Ops and related technologies.Qualifications:Bachelors or Masters degree in Computer Science, Data Engineering, Machine Learning, or a related field.Minimum of 4+ years experience with ML Ops tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).Proficiency in programming languages such as Python and experience with machine learning libraries (e.g., TensorFlow, PyTorch).Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for deploying ML solutions.Knowledge of version control systems (e.g., Git, Azure Repos) and CI/CD methodologies.Strong problem-solving skills and ability to work collaboratively in a fast-paced environment.Preferred Qualifications:Experience deploying and managing machine learning models in production environments.Knowledge of monitoring and logging tools.Familiarity with data engineering concepts and tools (e.g., Apache Airflow, Apache Spark).