Description :

We are seeking a hands-on MLOps Engineer with 36 years of experience in machine learning infrastructure, automation, and deployment.

You will design and manage scalable, production-grade ML systems with a strong focus on Docker, Kubernetes, CI/CD, and cloud environments.

While this role primarily emphasizes MLOps and DevOps engineering, a solid understanding of machine learning workflows and exposure to Generative AI (GenAI) or Large Language Models (LLMs) is a strong plus.

Key Responsibilities :

- Design, build, and maintain end-to-end MLOps infrastructure for model training, deployment, and monitoring.

- Develop and manage CI/CD pipelines for ML workflows using Docker and Kubernetes.

- Automate model deployment, data pipeline orchestration, and monitoring for ML systems in production.

- Collaborate with data scientists and ML engineers to operationalize ML models and ensure robust production performance.

- Implement Infrastructure-as-Code (IaC) using tools like Terraform, Helm, or Cloud Deployment Manager.

- Set up monitoring, logging, and alerting for model performance, drift, and system health.

- Optimize resource utilization and ensure high scalability and reliability across distributed ML workloads.

- (Good to have) Support GenAI/LLM-based applications, including RAG (Retrieval-Augmented Generation) pipelines and vector database integrations.

Key Requirements :

Core Skills :

- 3 to 6 years of hands-on experience in MLOps .

- Strong proficiency in Python and scripting for automation and tooling.

- Deep understanding of the ML lifecycle from data preparation to model deployment and monitoring.

- Expertise with Docker (containerization) and Kubernetes (orchestration).

- Experience with CI/CD pipelines and tools like GitHub Actions, Jenkins, ArgoCD, or GitLab CI.

- Familiarity with MLOps platforms such as MLflow, Kubeflow, Airflow, or Vertex AI Pipelines.

- Experience with cloud platforms GCP preferred, AWS or Azure acceptable.

- Working knowledge of monitoring and observability tools (Prometheus, Grafana, ELK, or Cloud Monitoring).