Senior MLOps Engineer

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

📋 Description

• Design and oversee scalable machine learning infrastructure on GCP utilizing Vertex AI, GKE, GCS, Cloud Run, and GPU/TPU compute resources.

• Manage the complete deployment lifecycle for machine learning models.

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

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

• Establish monitoring systems for overall system health and ML-specific metrics, such as drift, prediction accuracy, and shifts in data distribution.

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

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

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

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

⛳️ Requirements

• A minimum of 5 years of practical experience in designing, deploying, and maintaining production ML workloads within cloud environments.

• Extensive hands-on experience with Google Cloud Platform, including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations.

• Proficiency with Docker, Kubernetes/GKE, Triton Inference Server, vLLM, and MLflow.

• Demonstrated experience with Airflow, Vertex AI Pipelines, GitHub Actions, and ArgoCD.

• Strong background in managing cloud resources using Terraform.

• Expertise in Python and SQL for scripting, automation, API development, and data manipulation.

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

• Experience executing large-scale LLM or deep learning inference/training tasks.

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

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

🏝️ Benefits

• Opportunity to explore new technologies, products, and markets in a dynamic, growth-focused environment.

• Collaborate with skilled individuals in an inclusive company where people are valued.

• Individual contributions are recognized, allowing employees to see their impact.