Senior MLOps Engineer

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

📋 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 computing instances.

• Manage the complete deployment lifecycle for machine learning models.

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

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

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

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

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

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

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

⛳️ Requirements

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

• Extensive practical knowledge of Google Cloud Platform (GCP), including Vertex AI, Cloud Storage, GKE, Cloud Run, and IAM/VPC configurations.

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

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

• Strong experience 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 (Grafana, Prometheus, GCP Cloud Monitoring, or specialized ML observability frameworks).

• Experience in executing large-scale LLM or Deep Learning inference/training workloads.

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

• Knowledge of feature stores (e.g., Feast, Vertex AI Feature Store).

🏝️ Benefits

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

• Collaborate with talented individuals in a company that values its people.

• Be part of an inclusive community dedicated to preventing discrimination and harassment.