Job Title - ML Ops Engineer

Key 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:

• Bachelor’s or Master’s 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).