Machine Learning Operations Engineer

ML Ops Engineer

Required Skills: • Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). • Experience with containerization (Docker) and orchestration (Kubernetes). • Familiarity with cloud platforms (AWS, Azure, GCP) and ML services. • Expertise in CI/CD tools (GitHub Actions, Jenkins, Argo). • Knowledge of feature stores, model registries, and ML observability tools. • Understanding of data versioning and experiment tracking (MLflow, DVC). Experience: • 5–8 years of experience in software engineering or data engineering, with at least 3+ years in MLOps. Preferred Qualifications: • Experience with large-scale ML systems and distributed training. • Familiarity with GenAI model deployment and optimization. • Strong problem-solving and debugging skills in production environments Key Responsibilities: • Develop and maintain CI/CD pipelines for ML models and data workflows. • Automate model training, testing, deployment, and rollback processes. • Implement monitoring and alerting for model performance and data drift. • Optimize infrastructure for cost, scalability, and reliability (cloud or hybrid environments). • Collaborate with data scientists and software engineers to integrate ML models into production. • Ensure compliance with security, governance, and reproducibility standards.