AI engineer for MLOps and GPU computing

Deutsche Telekom; Warszawa; On-site; PLN 60,000 - 110,000; Part time

Job summary

Deutsche Telekom is seeking a part-time AI/ML Engineer to design and deploy production-grade AI/ML solutions. You will build MLOps pipelines, automate training and deployment, and monitor models while coordinating with data scientists, software engineers, and business stakeholders.

The role emphasizes GPU resource optimization and scalable architectures. The candidate should have 3+ years of AI/ML experience, strong Python, and hands-on with PyTorch/TensorFlow, Scikit-learn, Hugging Face, and

Qualifications

• 3+ years of experience in AI/ML projects.
• Strong Python and ML framework knowledge.
• Practical experience with MLOps tools and CI/CD pipelines.
• Experience with distributed AI/ML environments and GPUs.

Responsibilities

• Design and develop AI/ML solutions for production.
• Build and maintain MLOps pipelines for CI/CD of models.
• Automate model training, deployment, and monitoring.
• Managing end-to-end ML lifecycle and governance.
• Collaborate with data scientists, software engineers and stakeholders.
• Optimize GPU-based compute resources and performance.
• Monitor data drift and model quality; document architecture.

Skills

Python

PyTorch

TensorFlow

Scikit-learn

Hugging Face

CI/CD tooling

Docker

Kubernetes

MLflow

Kubeflow

Git

Tools

GitHub Actions

Azure DevOps

GitLab CI

Docker

Kubernetes

NVIDIA GPU Operator

KServe

Job description

The role focuses on designing, deploying, and maintaining AI/ML solutions in production environments, combining Machine Learning Engineering, MLOps, and GPU infrastructure administration and optimization to support model training and inference workloads.

Задачи:

• Design and develop AI and Machine Learning solutions
• Build and maintain MLOps pipelines for CI/CD of ML models
• Automate model training, deployment, and monitoring processes
• Manage the end-to-end Machine Learning Lifecycle
• Collaborate closely with Data Scientists, Software Engineers, and business stakeholders
• Optimize compute resource utilization, particularly in 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

Требования:

• At least 3 years of experience in AI/ML projects
• Strong Python skills
• Practical experience with PyTorch or TensorFlow, Scikit-learn, and Hugging Face
• Experience with MLOps technologies, including MLflow, Kubeflow, and Azure Machine Learning, Vertex AI, or SageMaker
• Experience with CI/CD tools such as GitHub Actions, Azure DevOps, or GitLab CI
• Experience with Docker and Kubernetes
• Practical 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, problem-solving mindset
• Strong organizational and collaboration skills in agile environments
• Будет плюсом: 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

Условия:

Part-time position