Description :
We are looking for a skilled DevOps Engineer with strong MLOps expertise to join our team. The ideal candidate will have hands-on experience in AWS Cloud, scripting, and automation, along with proven skills in deploying and managing machine learning pipelines in production.
Key Responsibilities :
- Design, implement, and maintain CI / CD pipelines for ML model training, testing, and deployment.
- Manage containerization & orchestration using Docker and Kubernetes.
- Automate infrastructure provisioning using Implement and manage MLOps tools (Kubeflow, MLflow, Airflow, SageMaker, etc.).
- Ensure scalability, reliability, and monitoring of ML / AI production systems.
- Work closely with data scientists and ML engineers to operationalize ML models.
- Develop automation scripts in Python / Bash to streamline workflows.
- Implement and manage AWS cloud services (EC2, S3, EKS / ECS, Lambda, RDS, IAM, Secrets Manager).
- Ensure compliance with security, cost optimization, and performance best practices.
Required Skills & 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 (GitLab CI / CD, Jenkins, ArgoCD).Proficiency in Docker & Kubernetes for container orchestration.Experience with MLOps tools : Kubeflow, MLflow, Airflow, SageMaker, or similar.Strong scripting / programming skills in Python and Bash.Experience with monitoring / logging tools (Prometheus, Grafana, ELK stack).Familiarity with data version control (DVC, Git) and feature stores is a plus.Excellent troubleshooting and problem-solving skills.Good to Have :
Experience with multi-cloud (GCP / Azure ML).Familiarity with TorchServe, BentoML, KFServing for model serving.Knowledge of security & compliance in ML pipelines.(ref : hirist.tech)