Senior MLOps Engineer - Cloud Infrastructure

Description :

Key Responsibilities :

- Build automated CI/CD pipelines for machine learning workflows and production deployments.

- Develop and manage end-to-end ML lifecycle processes including experimentation, versioning, validation, deployment, and monitoring.

- Collaborate with Data Scientists and ML Engineers to productionize machine learning models efficiently.

- Implement model monitoring systems to track drift, accuracy, latency, and system performance.

- Manage Kubernetes-based infrastructure and containerized ML workloads using Docker.

- Optimize infrastructure for scalability, reliability, security, and cost efficiency.

- Build feature stores, model registries, and reproducible training pipelines.

- Integrate ML systems with cloud platforms such as AWS, Azure, or GCP.

- Establish governance, observability, logging, and alerting mechanisms for ML platforms.

- Automate infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or

CloudFormation.

- Ensure compliance with data security, privacy, and operational best practices.

- Mentor junior engineers and drive MLOps best practices across teams.

Required Skills & Qualifications :

- 5+ years of experience in MLOps, DevOps, Data Engineering, or Machine Learning Engineering.

- Strong hands-on experience with machine learning deployment and operationalization.

- Expertise in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

- Strong experience with Kubernetes, Docker, and container orchestration.

- Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or ArgoCD.

- Hands-on experience with ML orchestration tools such as Kubeflow, MLflow, Airflow, SageMaker, or Vertex

AI.

- Strong knowledge of cloud platforms including Amazon Web Services, Microsoft Azure, or Google Cloud

Platform.

- Experience with monitoring and observability tools such as Prometheus, Grafana, ELK Stack, or Datadog.

- Knowledge of Infrastructure as Code tools like Terraform or Ansible.

- Understanding of data pipelines, feature engineering workflows, and distributed systems.

- Strong problem-solving, debugging, and system design skills