T Hub - AI Engineer ML Ops & GPU computing

About the project

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.

Your 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.

Our requirements

• 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.

Optional

• 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.

Recruitment stages

• Let’s meet to better understand each other's expectations.
• Hiring Manager will receive your application after meeting.
• Technical meetings with the Hiring Manager and/or team.
• Time to decide!

T-Mobile

Working at T-Mobile’s T Hub will offer you an unique and highly rewarding experience on IT market. As a leader in the telecommunications industry, T-Mobile not only provides a platform to hone your technical skills but also empowers you to be a catalyst for innovation. You'll have the opportunity to work at the forefront of cutting-edge technologies, from 5G to IoT and AI, shaping the future of connectivity. Our commitment to fostering a diverse and inclusive workplace means you'll collaborate with a wide range of talented professionals, learning and growing together. T-Mobile encourages a culture of continuous learning and development, offering mentorship and support to help you thrive in your career.

This is how we work