Machine Learning Engineer Operation [Talent Pool all Level]

The ML Ops role is responsible for deploying, automating, and maintaining machine learning and AI models across the enterprise. This role ensures that AI models are efficiently transitioned from experimentation to production and deliver consistent, scalable performance. The position builds and manages CI/CD pipelines, oversees model monitoring and retraining, and ensures models meet governance, security, and responsible AI standards. ML Ops works to ensure that every model deployed is stable, reliable, and aligned with business use case impact goals. This role plays a vital part in operationalizing AI at scale — automating model lifecycles, reducing time-to-production, and enabling the organization to act on AI insights in real time.Responsibilities:Deploy AI/ML models from development to production securely and efficiently.Build and manage automated ML pipelines for deployment, retraining, and rollback.Continuously monitor model performance (drift, accuracy, latency) and retrain when necessary.Manage compute, storage, and API optimization for AI workloads.Ensure production models comply with Responsible AI and governance standards.Work closely with Data Scientists and Value Assurance teams to align deployment with business use case requirements.Qualifications:1. Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.2. 5–8 years of experience in ML Ops, DevOps, or AI infrastructure management.3. Hands-on expertise in ML Ops frameworks (Kubeflow, MLflow, Airflow, Vertex AI, or similar).4. Strong knowledge of containerization, orchestration, and cloud platforms (GCP, AWS, or Azure).5. Experience building CI/CD pipelines for AI models and managing model drift monitoring.6. Understanding of responsible AI, data privacy, and compliance requirements in production environments.