Senior Software Engineer (Backend) – ON-SITE

DAZN
  • Katowice
  • Post Date: 3 stycznia, 2025
    • 39349
  • Applications 0
  • Views 7
Job Overview

logoAs a Lead NLP Engineer, you’ll play a key role in designing and implementing machine learning solutions. This includes processing and analyzing diverse data sets, building robust entity extraction
systems, and optimizing retrieval technologies for complex document structures. Your work will directly influence real-world applications and streamline workflows for an industry with immense potential.
Key Responsibilities:Design, implement, and maintain RAG pipelines to enable contextual and domain-specific AI responses.Develop and fine-tune generative models (e.g., GPT, BERT variants, T5) to ensure high-quality, context-aware
outputs.Integrate and manage external knowledge bases and APIs for real-time retrieval tasks.Optimize the performance of retrieval and generation models for scalability and low latency.Collaborate with data scientists and
product teams to align technical development with business objectives.Perform rigorous testing, debugging, and validation to ensure the reliability and accuracy of NLP pipelines.Stay updated with the latest advancements
in NLP, RAG frameworks, and transformer architectures.
Required Qualifications:Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field (Master’s degree is a plus).Minimum of
8 years of experience in NLP-focused roles.Proven experience building and deploying RAG pipelines in production.Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.Strong understanding of transformer-based
models (e.g., GPT, BERT, T5) and their architectures.Experience with retrieval systems (e.g., Elasticsearch, FAISS, or Weaviate) and retriever training.Familiarity with large language models (LLMs) and fine-tuning techniques.Knowledge
of tokenization, embeddings, and vector similarity measures.Experience with API integration for third-party knowledge sources
Preferred Qualifications:Hands-on experience with frameworks and libraries such as Hugging Face Transformers, LangChain, or Haystack.Knowledge of Knowledge Graphs and their integration with RAG pipelines.Experience in building
conversational agents or chatbots with advanced contextual understanding.Familiarity with MLOps practices for scalable deployment of NLP systems.Contributions to research papers, open-source projects, or NLP communities.

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