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Senior MLOps Engineer

3 weeks, 3 days ago
Poland
Full-time
Senior
Machine Learning Engineer
Software Development
Apache Airflow Argo CD CI/CD Deep Learning Docker GCP GitHub Actions Grafana Kubernetes Machine Learning Microservices MLflow MLOps Prometheus Python SQL Terraform Vertex AI
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Point Wild

Point Wild

Point Wild specializes in providing comprehensive online security solutions, including consumer VPN and antivirus services, while also supporting businesses with tools to enhance customer retention and address data breaches.

Internet Software & Services
View All Jobs 7

Description

  • Architect and manage scalable ML infrastructure on GCP using Vertex AI, GKE, GCS, Cloud Run, and GPU/TPU compute.
  • Own the end-to-end deployment and serving lifecycle for machine learning models.
  • Build high-throughput, low-latency inference services using containers and tools such as Triton, vLLM, and MLflow.
  • Develop automated, reproducible pipelines for model training, testing, evaluation, and deployment.
  • Implement monitoring for system health and ML metrics, including drift, accuracy, and data distribution shifts.
  • Provide scalable training environments, optimized runtimes, and standardized deployment templates for AI teams.
  • Integrate ML pipelines with feature stores, dataset versioning, and batch or streaming data workflows.
  • Transition AI prototypes and notebooks into secure, resilient, auto-scaling production microservices.

Requirements

  • 5+ years of experience designing, deploying, and maintaining production ML workloads in cloud environments.
  • Deep practical experience with GCP, including Vertex AI, Cloud Storage, GKE, Cloud Run, IAM, and VPC configuration.
  • Expertise with Docker, Kubernetes/GKE, and serving tools such as Triton, vLLM, and MLflow.
  • Experience with Airflow, Vertex AI Pipelines, GitHub Actions, and ArgoCD.
  • Experience managing cloud infrastructure with Terraform.
  • Proficiency in Python and SQL for automation, API development, scripting, and data manipulation.
  • Hands-on experience with logging, telemetry, and ML observability tools such as Grafana, Prometheus, and GCP Cloud Monitoring.
  • Experience with large-scale LLM or deep learning workloads (preferred).
  • GCP Professional Machine Learning Engineer or Cloud Architect certification (preferred).
  • Familiarity with feature stores such as Feast or Vertex AI Feature Store (preferred).

Benefits

  • Opportunity to solve real customer problems in cybersecurity.
  • High individual visibility and impact in a scrappy, nimble organization.
  • Opportunities to learn new technologies, products, and markets as the company grows.
  • Inclusive workplace committed to equal opportunity and freedom from discrimination and harassment.

Interested in this position?

Apply directly on the company website

Apply Now

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