T Hub - AI Engineer ML Ops & GPU computing

MagentaTeam; Województwo mazowieckie; On-site; PLN 120,000 - 180,000; Full time

Benefits offered by this job

No dress code

Insurance packages (medical, sport, l&

Preferential terms for products andos

Job summary

MagentaTeam is seeking an experienced AI Engineer to design, deploy, and maintain AI/ML solutions in production. The role blends ML Engineering, MLOps, and GPU infrastructure administration to support training and inference workloads.

Responsibilities include building pipelines, automating training/deployment/monitoring, managing ML lifecycle, collaborating with data scientists and engineers, optimizing GPU resources, and documenting best practices.

Qualifications

• Minimum 3 years of experience in AI/ML projects.
• Strong proficiency in Python.
• Hands-on experience with PyTorch or TensorFlow; Scikit-learn.
• Experience with MLOps technologies such as MLflow.
• Experience with Docker and Kubernetes.
• Experience with distributed AI/ML environments and compute clusters.
• Good understanding of cloud platforms (Azure, AWS, or GCP) or on‑premise systems.
• English proficiency at B2 level or higher.
• Ability to translate business requirements into technical implementations.
• Analytical mindset with problem-solving approach.
• Passion for experimenting with emerging AI technologies.
• Strong organizational and collaboration skills in Agile environments.

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.

Skills

Python

MLOps

Docker

Kubernetes

PyTorch

TensorFlow

Scikit-learn

Git

Cloud platforms

English (B2+)

RAG

Vector databases

Agentic AI

Spark/Databricks/Ray

Education

Tools

MLflow

Docker

Kubernetes

Ray

Kubeflow

NVIDIA GPU Operator

KServe

Git

Job description

T Hub - AI Engineer ML Ops & GPU computing

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.

WHAT SKILLS WILL BE APPRECIATED?

• Minimum 3 years of experience in AI/ML projects.
• Strong proficiency in Python.
• Hands-on experience with:
• PyTorch or TensorFlow
• Scikit-learn
• Experience with MLOps technologies:
• MLflow
• 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.

No dress code - you can just be yourself here

Medical, sport and life insurance packages at preferential terms

Access to our products and services at preferential terms

Employment contract-based cooperation

Know Talent - receive training or financial bonus for recommending new employees