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# Machine Learning DevOps

- **Company:** Pathways @
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Computer Programming, Continuous Integration, Linux, Github, Python (Programming Language), Machine Learning, Tensorflow, Prometheus, Azure Machine Learning, Shell Script, Data Logging, Google Cloud, Data Ingestion, Pytorch, Large Language Models, Grafana, Cloudformation, Containerization, Gitlab-ci, Scikit Learn, Kubernetes, Information Technology, Slurm, Machine Learning Operations, Cloudwatch, Terraform, Software Version Control, Docker, Jenkins
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-devops-cloud-and-compute-cluster-rd-support-pathway-7447231

## About the Role

* Very good familiarity with Linux, shell scripts, and cluster configuration scripts as the basic work tool.
 * Proficiency in workload management, containerization and orchestration (Slurm, Docker, Kubernetes).
 * Solid grasp of CI/CD tools and workflows (GitHub Actions, Jenkins, Gitlab CI, etc.).
 * Cloud infrastructure knowledge (AWS, GCP, Azure) - especially in ML services (e.g., SageMaker Hyperpod, Vertex AI).
 * Familiarity with monitoring/logging tools (Grafana, CloudWatch, Prometheus, Loki).
 * Experience with infrastructure as code (Terraform, CloudFormation, cluster-toolkit).
 * Experience with ML pipeline orchestration tools (e.g., MLflow, Kubeflow, Airflow, Metaflow).
 * Programming skills in Python (with exposure to ML libraries like TensorFlow, PyTorch).
 * Experience with cluster, systems, and networks administration.
 * Willingness to learn.

This position holds a minimum requirement of a BSc in Computer Science or Information Technology.

## Description

* Optimize infrastructure for ML training and inference (e.g., GPUs, distributed compute).
 * Automate and maintain ML/LLM pipelines (data ingestion, training, validation, deployment).
 * Manage model versioning, reproducibility, and traceability.
 * Work with terabyte-large datasets.
 * Implement ML-centric CI/CD practices.
 * Monitor model performance and data drift in production.
 * Collaborate with machine learning engineers, software engineers, and platform teams.

The role focuses on operationalizing machine learning models, ensuring scalability, reliability, and automation across the ML lifecycle.


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