DevOps Engineer (MLOPs)

As a skilled DevOps Engineer with strong MLOps expertise, you will be responsible for the following key responsibilities: - Designing, implementing, and maintaining CI/CD pipelines for ML model training, testing, and deployment. - Managing containerization & orchestration using Docker and Kubernetes. - Automating infrastructure provisioning using Terraform/CloudFormation. - Implementing and managing MLOps tools such as Kubeflow, MLflow, Airflow, SageMaker, etc. - Ensuring scalability, reliability, and monitoring of ML/AI production systems. - Working closely with data scientists and ML engineers to operationalize ML models. - Developing automation scripts in Python/Bash to streamline workflows. - Implementing and managing AWS cloud services like EC2, S3, EKS/ECS, Lambda, RDS, IAM, Secrets Manager. - Ensuring compliance with security, cost optimization, and performance best practices. In order to be successful in this role, you are required to have the following skills and experience: - 7+ years of experience as a DevOps/MLOps Engineer. - Strong hands-on expertise in AWS Cloud (compute, storage, serverless, IAM). - Solid knowledge of CI/CD tools such as GitLab CI/CD, Jenkins, ArgoCD. - Proficiency in Docker & Kubernetes for container orchestration. - Experience with MLOps tools like Kubeflow, MLflow, Airflow, SageMaker, or similar. - Strong scripting/programming skills in Python and Bash. - Experience with monitoring/logging tools such as Prometheus, Grafana, ELK stack. - Familiarity with data version control tools like DVC, Git, and feature stores is a plus. - Excellent troubleshooting and problem-solving skills. Additionally, it would be good to have the following: - Experience with multi-cloud platforms like GCP/Azure ML. - Familiarity with TorchServe, BentoML, KFServing for model serving. - Knowledge of security & compliance in ML pipelines.,