MLOps Engineer

Intelligenz IT; Polska; On-site; PLN 180,000 - 240,000; Full time

Job summary

IntelligenzIT is seeking an experienced MLOps Engineer in Poland to build and operate scalable ML infrastructure on Google Cloud Platform. You will own deployment pipelines, model serving, and telemetry, driving automation and reliability across Kubernetes-based environments.

You will collaborate with data scientists and software engineers to optimize workflows, implement monitoring, logging, and CI/CD for ML workloads, and support production-grade ML systems at scale.

Qualifications

• Experience deploying and operating production ML systems.
• Strong cloud infrastructure and automation skills.
• Hands-on with Kubernetes-based deployments.

Responsibilities

• Deploy and operate scalable ML inference services.
• Build and optimize ML deployment pipelines.
• Implement monitoring and observability for ML services.

Skills

MLOps

Cloud infrastructure

Deployment automation

Model serving

Observability

Tools

Kubernetes

GKE

Helm

Istio

NVIDIA Triton

Prometheus

Grafana

Apache Airflow

Cloud Composer

GitHub Actions

Google Cloud Build

Git

Google Cloud Storage

Job description

We are looking for an experienced MLOps Engineer with strong expertise in Google Cloud Platform, Kubernetes, ML model deployment, inference serving, and cloud-native automation to build and operate scalable machine learning infrastructure.

• Kubernetes / GKE, Helm, and Istio
• CI/CD pipelines and GitHub Actions
• ML model deployment and model serving
• NVIDIA Triton Inference Server
• Monitoring and observability using Prometheus and Grafana
• Apache Airflow / Cloud Composer
• Git, containerisation, and Google Cloud Build
• Deploy, serve, and manage machine learning models on GCP.
• Build and maintain scalable Kubernetes/GKE infrastructure.
• Develop and optimise CI/CD pipelines for ML deployment workflows.
• Configure inference serving using NVIDIA Triton Inference Server.
• Implement monitoring, logging, and observability for ML services.
• Orchestrate data and ML workflows using Apache Airflow / Cloud Composer.
• Support reliable, automated, and production-ready MLOps processes.

Good to Have

• GPU workloads and Managed Instance Groups (MIG)
• Retrieval serving and semantic search architectures
• Basic understanding of Machine Learning and Generative AI
• Exposure to ColBERT, RAGatouille, or RAG-based search solutions
• Google Cloud Storage and versioned model/index management
• Multi-market or large-scale data-processing pipelines

A hands-on MLOps professional experienced in deploying and operating production ML systems, with strong cloud infrastructure, automation, Kubernetes, and model-serving expertise.