MLOps Engineer

Job Summary (List Format): - Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring. - Lead implementation of containerized ML workloads using Kubernetes. - Collaborate with data scientists and engineers to productionize ML models. - Automate model lifecycle management, including versioning, rollback, and performance tracking. - Ensure high availability, security, and compliance of ML systems. - Develop infrastructure as code using tools such as Terraform or Helm. - Establish and enforce best practices for model governance and reproducibility. - Requires a Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred). - 5 10 years of experience in MLOps, DevOps, or software engineering. - Extensive experience with Kubernetes and container orchestration. - Proficient in Python and Bash scripting. - Experience with ML frameworks (TensorFlow, PyTorch, or Scikit-learn). - Familiarity with cloud platforms (AWS, Azure, GCP). - Knowledge of CI/CD tools and monitoring systems. - Preferred: Experience with Kubeflow, MLflow, data versioning tools (DVC, LakeFS), and model compliance frameworks. - English fluency required for international collaboration. - Background verification process includes employment check and police clearance certificate. - Candidate should be an Inhouse Bench resource. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.