DataOps Engineer

As a DataOps Engineer, you will be responsible for designing and maintaining scalable ML model deployment infrastructure using Kubernetes and Docker. Your role will involve implementing CI/CD pipelines for ML workflows, ensuring security best practices are followed, and setting up monitoring tools to track system health, model performance, and data pipeline issues. You will collaborate with cross-functional teams to streamline the end-to-end lifecycle of data products and identify performance bottlenecks and data reliability issues in the ML infrastructure. To excel in this role, you should have strong experience with Kubernetes and Docker for containerization and orchestration, hands-on experience in ML model deployment in production environments, and proficiency with orchestration tools like Airflow or Luigi. Familiarity with monitoring tools such as Prometheus, Grafana, or ELK Stack, knowledge of security protocols, CI/CD pipelines, and DevOps practices in a data/ML environment are essential. Exposure to cloud platforms like AWS, GCP, or Azure is preferred. Additionally, experience with MLflow, Seldon, or Kubeflow, knowledge of data governance, lineage, and compliance standards, and understanding of data pipelines and streaming frameworks would be advantageous in this role. Your expertise in data pipelines, Docker, Grafana, Airflow, CI/CD pipelines, orchestration tools, cloud platforms, compliance standards, data governance, ELK Stack, Kubernetes, lineage, ML, streaming frameworks, ML model deployment, and DevOps practices will be key to your success in this position.,