Machine Learning Engineer

• Build and automate CI/CD pipelines for machine learning models and data workflows using GitHub Actions, GitLab CI, or Jenkins.
• Architect and manage containerized microservices and orchestration platforms for ML workloads using Docker and Kubernetes (EKS, GKE, AKS).
• Implement and manage ML lifecycle, tracking, and versioning tools such as MLflow, DVC, or Kubeflow.
• Provision, configure, and maintain ML cloud infrastructure using Infrastructure as Code tools like Terraform.
• Establish monitoring, alerting, and logging systems for deployed models using Prometheus, Grafana, or Arize to track data drift, model performance, and operational health.
• Collaborate with data scientists and software engineers to transition ML models from experimental scripts into secure, low-latency, scalable production APIs.
• Design, build, and maintain infrastructure, pipelines, and tools enabling seamless machine learning deployment and operation across enterprise environments.
• Own ML workflows end-to-end, from automated training and versioning to production monitoring and continuous integration.

Explore Senior MLOps Engineer at Factor Funding Company. Review requirements and application details.

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