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IBM: Senior AI Engineer (Kraków)

Senior AI Engineer

Bezpośrednia
  • IBM
  • Kraków
  • 30.10.2025

Podsumowanie oferty:

  • Branża

    Konsulting / Software House / Usługi IT
  • Rodzaj umowy

    UoP
  • Doświadczenie

    Senior
  • Technologie i Narzędzia

    NLP, Python, PyTorch, TensorFlow, transformer architectures, JAX, MLOps, data processing pipelines, model deployment, monitoring

Opis oferty:

IBM Automation and AI group is seeking an experienced AI/ML Engineer specializing in Large Language Models (LLMs) to join our AI/ML Center of Excellence team. In this role, you will collaborate with cross-functional product teams to implement cutting-edge AI capabilities across IBM's Automation product portfolio.

Your role and responsibilities:

  • Data Collection and Management for LLM Evaluation and Training
  • LLM Integration and Implementation
  • AI/ML Best Practices and Innovation

ABOUT BUSINESS UNIT: IBM Software infuses core business operations with intelligence—from machine learning to generative AI—to help make organizations more responsive, productive, and resilient.

YOUR LIFE @ IBM: In a world where technology never stands still, we understand that dedication to our clients' success, innovation that matters, and trust and personal responsibility in all our relationships live in what we do as IBMers.

Are you ready to be an IBMer?

ABOUT IBM: IBM’s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.

Wymagania:

  • Demonstrated expertise with NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design.
  • Exceptional programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
  • Extensive Experience with data processing pipelines and working with large datasets
  • Deep knowledge of MLOps practices and tools for model deployment and monitoring
  • Ability to work independently and collaborate across diverse teams
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