✅ Job Description: ML Infrastructure / MLOps EngineerJoin a high-impact, transformative Data & AI platform initiative designed to modernize enterprise capabilities and enable real-time, data-first decision-making. This project spans AI, MLOps, data governance, and platform scalability, with a strong focus on building intelligent automation at scale.We’re seeking an experienced MLOps Engineer or ML Infrastructure Engineer to help build and scale the foundation for production-grade ML – from training pipelines to monitoring and drift detection. Responsibilities:Design and build robust infrastructure for training, serving, and monitoring ML modelsEnsure reproducibility, traceability, and model versioningSupport drift detection, logging, and automated validationCollaborate with AI and data science teams to enable GenAI-powered recommendations through optimized MLOps pipelinesEnsure efficient deployment using CI/CD best practices and scalable orchestration tools Requirements:4+ years of experience in ML infrastructure, MLOps, or ML engineeringDeep knowledge of model deployment, monitoring, versioning, and productionizing ML modelsStrong understanding of tools such as MLflow, Kubeflow, Airflow, or equivalentsProficiency in Python and infrastructure-as-code (e.g., Terraform, Docker, Kubernetes)Advanced English➕ Nice to Have:Experience with SHAP, Generative AI (GenAI), and explainability toolingHands-on familiarity with pipeline orchestration (e.g., Argo, Prefect)Experience building solutions in GCP, AWS, or Azure AI stacks