Senior MLOps Engineer: Scale AI on GCP & Production

Point Wild; Warszawa; On-site; PLN 320,000 - 540,000; Full time

Job summary

Point Wild is seeking a Senior MLOps Engineer to design, build, and maintain scalable ML infrastructure on Google Cloud Platform. You will collaborate with AI Researchers and Backend teams to bridge experimentation and production systems.

The role focuses on deployment, monitoring, and automation across pipelines, with a strong emphasis on reliability, scalability, and cutting-edge ML tooling.

Qualifications

• 5+ years of hands-on experience designing, deploying, and maintaining production ML workloads in cloud environments.
• Deep, practical experience with Google Cloud Platform (GCP) including Vertex AI, GKE and Cloud Run.
• Proficiency in Python and SQL for scripting, automation, and data manipulation.

Responsibilities

• Architect and manage scalable GCP-based ML infrastructure using Vertex AI, GKE, GCS, Cloud Run and GPU/TPU compute.
• Own end-to-end model deployment and serving with high-throughput, low-latency inference services.
• Build automated, reproducible CI/CD/CT pipelines for training, testing, evaluation, and deployment.
• Implement robust production observability for system health and ML-specific metrics.
• Provide scalable training environments and standardized deployment templates for AI engineers.
• Collaborate with Data Engineers on feature stores, dataset versioning, and data workflows.
• Lead the transition of PoCs to production microservices with a secure, auto-scaling architecture.

Skills

MLOps

GCP

Docker

Kubernetes

Python

SQL

Tools

Vertex AI

GKE

Cloud Run

Triton Inference Server

MLflow

Airflow

Job description

Point Wild is seeking a Senior MLOps Engineer to design, build, and maintain scalable ML infrastructure on Google Cloud Platform. You will collaborate with AI Researchers and Backend teams to bridge experimentation and production systems.

The role focuses on deployment, monitoring, and automation across pipelines, with a strong emphasis on reliability, scalability, and cutting-edge ML tooling.