Senior MLOps Engineer

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

📋 Description

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

• Take responsibility for the complete deployment lifecycle of machine learning models.

• Develop high-throughput, low-latency inference services using containerization and specialized serving frameworks such as Triton Inference Server, vLLM, and MLflow.

• Create automated, reproducible pipelines for model training, testing, evaluation, and deployment using Airflow, Vertex AI Pipelines, and GitHub Actions.

• Implement monitoring solutions for system health and machine learning-specific metrics to enable automated retraining triggers.

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

• Collaborate with Data Engineers to integrate model pipelines with feature stores, dataset versioning, and both stream and batch data processing workflows.

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

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

⛳️ Requirements

• Minimum of 5 years of practical experience in designing, deploying, and maintaining production-level ML workloads in cloud environments.

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

• Proficient in containerization technologies (Docker, Kubernetes/GKE) and specialized serving tools (Triton, vLLM, MLflow).

• Proven experience with workflow orchestrators (Airflow, Vertex AI Pipelines) and modern CI/CD tools (GitHub Actions, ArgoCD).

• Solid background in managing cloud resources using Terraform.

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

• Hands-on experience with logging, telemetry, and drift detection tools (Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks).

• Experience with running large-scale LLM or Deep Learning inference/training workloads.

• GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect certifications.

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

🏝️ Benefits

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

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

• Chance to make a tangible impact within a nimble and scrappy organization.