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. - 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.