Senior MLOps Engineer

This is a fully remote position, open to applicants in Estonia, +1 more country.

📋 Description

• Design and oversee scalable ML infrastructure on GCP utilizing Vertex AI, Google Kubernetes Engine, Google Cloud Storage, Cloud Run, and GPU/TPU compute instances.

• Manage the complete deployment lifecycle for machine learning models.

• Develop high-throughput, low-latency inference services through containerization and specialized serving frameworks.

• Create automated, reproducible pipelines for model training, testing, evaluation, and deployment.

• Establish monitoring for system health and ML-specific metrics, including feature drift, prediction accuracy, and shifts in data distribution.

• Deliver scalable training environments, optimized runtime infrastructure, and standardized deployment templates for AI and research engineers.

• Collaborate with Data Engineers on feature stores, dataset versioning, and workflows for stream and batch data processing.

• Guide the evolution of AI prototypes and notebooks into robust, secure, auto-scaling microservices.

• Work alongside AI Researchers, Data Engineers, and Backend teams to connect experimentation with enterprise-grade production systems.

⛳️ Requirements

• Minimum 5 years of hands-on experience in designing, deploying, and maintaining production ML workloads within cloud environments.

• In-depth practical knowledge of Google Cloud Platform, including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations.

• Proficiency in containerization technologies such as Docker and Kubernetes/GKE.

• Expertise in specialized serving tools like Triton, vLLM, and MLflow.

• Proven experience with Airflow and Vertex AI Pipelines.

• Demonstrated experience with contemporary CI/CD tools, including GitHub Actions and ArgoCD.

• Solid experience in managing cloud resources utilizing Terraform.

• Proficient in Python and SQL.

• Practical experience with logging, telemetry, and drift detection tools such as Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks.

• Experience with large-scale LLM or deep learning inference/training workloads is preferred.

• GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect certification is preferred.

• Familiarity with feature stores like Feast or Vertex AI Feature Store is preferred.

🏝️ Benefits

• Opportunity to address real customer challenges in cybersecurity.

• Chance to observe personal impact within a dynamic, agile organization.

• Career advancement and opportunities to explore new technologies, products, and markets.

• Collaborate with skilled colleagues in an inclusive environment.

• Commitment to non-discrimination and anti-harassment.

• Equal opportunity for all applicants regardless of protected characteristics.