Full-Time ML Platform Engineer

• Design and architect scalable, resilient platforms.
• Build and maintain robust platform frameworks for data science and machine learning.
• Collaborate with application development, data science, and SRE teams.
• Deepen expertise in core platform technologies like Kubernetes (EKS, AKS), Helm, Terraform, and GitOps tools (ArgoCD).
• Ensure seamless deployment and operation of platforms by building and maintaining CI/CD pipelines (GitLab, Azure Pipelines).
• Monitor, analyze, and optimize system performance and security using tools like Prometheus, Grafana, and Datadog.
• Improve platform reliability, scalability, and availability by applying SRE principles like SLOs and error budgets.
• Create and maintain comprehensive documentation for all platforms and frameworks.

• 5+ years of experience in software development or DevOps, with at least 2 years in platform engineering.
• Strong hands-on experience with Docker, Kubernetes, and GitOps tooling.
• Proficiency in cloud platforms, with experience in AWS and Azure.
• Familiarity with monitoring and observability tools like Corelogix, Prometheus, Grafana, Datadog, or OpenTelemetry.
• Solid understanding of CI/CD pipelines leveraging tools like GitLab or GitHub actions.
• Experience with Infrastructure as Code (Terraform, Ansible) and configuration management tools.
• Knowledge of networking concepts (TCP/IP, DNS, REST APIs) and API management tools.
• Experience with building and managing ML platforms is a plus, specifically with tools like MLflow or Kubeflow.
• Proficiency in scripting languages like Python or Bash.
• Excellent communication skills.
• Team-oriented mindset.
• A Bachelor’s degree in Computer Science or Computer Engineering.

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