Intetics

MLOps Engineer

Intetics

full-time

Posted on:

Location Type: Remote

Location: Ukraine

Visit company website

Explore more

✨ AI Apply
Apply

Tech Stack

AWSCloudDockerGrafanaKubernetesPrometheusTerraformUnity

About the role

  • Design and implement scalable, secure, and cost‑efficient MLOps solutions leveraging AWS and Databricks.
  • Automate ML deployment pipelines, reducing manual intervention and operational overhead.
  • Collaborate closely with data scientists to ensure solutions align with established MLOps architecture, best practices, and platform standards.
  • Integrate security controls and compliance requirements throughout the entire machine learning lifecycle.
  • Own and manage incidents end‑to‑end, from root cause analysis to prevention of future occurrences.
  • Contribute to software system architecture and the design of platform‑level components.
  • Build and optimize ML training, retraining, and inference pipelines, ensuring reliability and scalability.
  • Enhance observability with metrics, logging, tracing, and dashboards to ensure system visibility and performance.
  • Drive best practices in infrastructure automation, CI/CD, and cloud resource management across ML teams.

Requirements

  • Strong hands‑on experience with AWS architecture, including security best practices, IAM, networking, and cost optimization.
  • Proficiency with Databricks (essential): MLflow, Workflows, Feature Store, cluster management, Unity Catalog.
  • Experience with cloud‑managed ML platforms such as AWS SageMaker or Google Vertex AI.
  • Expert knowledge of Terraform / Terragrunt for multi‑cloud infrastructure provisioning and automation.
  • Deep expertise in Kubernetes, including autoscaling, GPU workloads, networking policies, and cluster optimization.
  • Practical experience with observability stacks such as Prometheus, Grafana, Loki, ELK.
  • Strong understanding of GitOps workflows and CI/CD tools (e.g., ArgoCD, FluxCD).
  • Solid knowledge of Docker security, container hardening, and secure container orchestration.
  • Advanced experience in MLOps practices for continuous training (CT), CI/CD for ML models, and automated deployment.
  • Familiarity with ML pipeline orchestration tools such as Kubeflow or Argo Workflows.
  • Experience with LLMOps, including frameworks such as Langfuse, ollama, vLLM, and supporting large‑scale inference.
  • Ability to contribute to architecture design, set platform standards, and mentor MLOps or ML engineers.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
MLOpsAWSDatabricksTerraformKubernetesDockerCI/CDML pipeline orchestrationobservabilityLLMOps
Soft Skills
collaborationincident managementroot cause analysismentoringarchitecture design