MLOps Engineer

Key Responsibilities Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring. Lead the 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 like Terraform or Helm. Establish and enforce best practices for model governance and reproducibility. Required Qualifications Bachelors degree in computer science, Engineering, or related field (masters preferred). 5 10 years of experience in MLOps, DevOps, or software engineering. Extensive experience with Kubernetes and container orchestration. Proficiency in Python and Bash scripting. Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Familiarity with cloud platforms (AWS, Azure, GCP). Knowledge of CI/CD tools and monitoring systems. Preferred Qualifications Experience with Kubeflow, MLflow, or similar platforms. Exposure to data versioning tools like DVC or LakeFS. Understanding of model explainability and compliance frameworks. Contributions to open-source MLOps projects. There will be a BGV process for this requirement including: Employment Check PCC Police Clearance Certificate (Criminal Record Check) English fluency (all teams work internationally, and English is the standard language). Candidate should be your 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.