MLOps Engineer

This is a fully remote position, open to applicants in Ukraine.

📋 Description

• Develop and execute scalable, secure, and cost-effective MLOps solutions utilizing AWS and Databricks.

• Streamline ML deployment pipelines through automation, minimizing manual intervention and operational burdens.

• Work in close collaboration with data scientists to ensure that solutions are in line with established MLOps architecture, best practices, and platform standards.

• Incorporate security measures and compliance requirements throughout the complete machine learning lifecycle.

• Take ownership of incidents from start to finish, conducting root cause analysis and implementing preventive measures for future occurrences.

• Contribute to the architecture of software systems and the design of platform-level components.

• Construct and enhance ML training, retraining, and inference pipelines to ensure reliability and scalability.

• Improve observability through metrics, logging, tracing, and dashboards, ensuring system visibility and optimal performance.

• Promote best practices in infrastructure automation, CI/CD, and cloud resource management across ML teams.

⛳️ Requirements

• Extensive hands-on experience with AWS architecture, including security best practices, IAM, networking, and cost efficiency.

• Proficient with Databricks (essential): MLflow, Workflows, Feature Store, cluster management, and 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.

• In-depth expertise in Kubernetes, including autoscaling, GPU workloads, networking policies, and cluster optimization.

• Practical experience with observability stacks like Prometheus, Grafana, Loki, and 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 like Langfuse, ollama, vLLM, and supporting large-scale inference.

• Ability to contribute to architectural design, establish platform standards, and mentor MLOps or ML engineers.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Opportunities for professional development and continuous learning.

• Flexible work arrangements and a supportive work environment.

• Access to cutting-edge technologies and resources.