AI Engineer (m/f/d) ML Ops & GPU Computing T Hub

Job ID: 3580441

AI Engineer (m/f/d) ML Ops & GPU Computing T Hub

Warsaw

Professional

Application deadline: 30.12.2026

Regular (Part Time Possible)

T-Hub Polska

Your Task

We are looking for an experienced AI Engineer who will be responsible for designing, deploying, and maintaining AI/ML solutions in production environments. The role combines expertise in Machine Learning Engineering, MLOps, and basic administration and optimization of GPU infrastructure supporting model training and inference workloads.

Key Responsibilities

• Design and develop AI and Machine Learning solutions.
• Build and maintain MLOps pipelines (CI/CD for ML models).
• Automate model training, deployment, and monitoring processes.
• Manage the end-to-end machine learning lifecycle (ML Lifecycle Management).
• Collaborate closely with Data Scientists, Software Engineers, and business stakeholders.
• Optimize computational resource utilization, particularly GPU-based environments.
• Monitor model performance, quality, and data drift.
• Contribute to the design of AI architectures on cloud platforms.
• Create technical documentation and establish engineering best practices.

Your Profile

• Minimum 3 years of experience in AI/ML projects.
• Strong proficiency in Python.
• Hands-on experience with:
• • PyTorch or TensorFlow
  • Scikit-learn
  • Hugging Face
• Experience with MLOps technologies:
• • MLflow
  • Kubeflow
  • Azure Machine Learning, Vertex AI, or SageMaker
  • CI/CD tools (GitHub Actions, Azure DevOps, GitLab CI)
• Experience with Docker and Kubernetes.
• Hands-on experience working with distributed AI/ML environments and compute clusters.
• Good understanding of cloud platforms (Azure, AWS, or GCP) or on-premise systems.
• Understanding of workload orchestration, resource allocation, and scaling for machine learning workloads.
• Experience with Git and modern DevOps practices.
• English proficiency at B2 level or higher.
• Strong ownership and accountability for delivered solutions.
• Ability to translate business requirements into technical implementations.
• Analytical mindset with a problem-solving approach.
• Passion for experimenting with emerging AI technologies.
• Strong organizational and collaboration skills in Agile environments.

Nice-to-Have

• Experience with LLMs and Generative AI solutions.
• Knowledge of RAG, Vector Databases, and Agentic AI.
• Experience with cloud-based GPU platforms (Azure AI, AWS EC2 P4/P5, GCP A3).
• Experience managing or operating GPU-enabled Kubernetes clusters.
• Experience with NVIDIA GPU Operator, KServe, Ray, Kubeflow, or similar technologies.
• Understanding of cluster monitoring and performance optimization in AI environments.
• Familiarity with Spark, Databricks, or Ray.