GenAI Engineer

Senior GenAI Engineer

Location: Katowice / Wrocław - 2 days in office / 3 days remote

Let us introduce you the job offer by EY GDS Poland – a member of the global integrated service delivery center network by EY.

The opportunity

We are seeking a highly skilled Senior GenAI Engineer with at least 3 years of experience in Data Science, Machine Learning and key areas such as NLP, Generative AI, LLMs and MLOps. The ideal candidate will design and implement advanced AI models and systems while leveraging modern frameworks like LangChain, LangGraph or agentic AI technologies. The role includes developing new AI approaches and solutions, experimenting with cutting-edge architectures and driving innovation across business use cases. Additionally, experience in data engineering and cloud platforms will be valuable in delivering scalable and high‑impact AI capabilities.

It would be beneficial to expand the description by adding more specific details about designing and implementing new AI solutions—particularly in the areas of Generative AI, LLM-based architectures, NLP techniques, and MLOps practices. Highlighting work on developing new approaches, optimizing AI models, and applying advanced frameworks such as LangChain, LangGraph, or Agentic AI frameworks would help better reflect the innovative and technical nature of this Senior GenAI Engineer role.

To qualify for the role, you must have

• 3+ years of experience in Data Science and Artificial Intelligence.
• Knowledge and experience in Generative AI (GenAI) including Agentic AI.
• Proficiency in Python programming language and frameworks like TensorFlow, PyTorch, Pandas or Scikit-Learn.
• Knowledge of at least one framework like LangChain, LangGraph, AutoGen, Semantic Kernel or other.
• Strong understanding of Natural Language Processes (NLP) techniques such as tokenization, string comparison and embeddings.
• Experience with cloud platforms such as Azure, AWS or GCP.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Ideally, you’ll also have

• Hands-on experience with tools such as Docker, Kubernetes, and Git or Azure DevOps to build and manage AI pipelines including MLOps practices, covering CI/CD, and monitoring of AI.
• Know trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.
• Implement monitoring and logging tools to ensure AI model performance and reliability.
• Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment.