Data Engineer

Main Responsibilities

• Build and deploy GraphRAG solutions integrating knowledge graphs (e.g., Neo4j) and vector databases (e.g., Weaviate).
• Develop Hybrid Retrieval systems using FAISS/Milvus + BM25.
• Fine-tune LLMs (GPT, LLaMA, Falcon) using LoRA/PEFT.
• Build scalable GenAI pipelines using Hugging Face, LangChain, Weaviate, and OpenAI APIs.
• Implement Weaviate’s Ignition Framework for enterprise-grade RAG (mandatory).
• Deploy solutions on AWS/GCP/Azure with strong MLOps practices.
• Evaluate and optimize retrieval and generation performance.
• Work closely with engineering and product teams to deliver production AI systems.
• Maintain strong documentation, monitoring, and optimization of AI pipelines.

Key Requirements

• 5–8+ years of hands-on AI/ML engineering with production deployments.
• Strong NLP + LLM experience:
  • LoRA/PEFT fine-tuning
  • Prompt engineering
  • Embeddings & retrieval optimization
• Practical experience with GraphRAG, knowledge graphs, and Neo4j.
• Mandatory: Experience with Weaviate’s Ignition Framework.
• Strong Python; PyTorch/TensorFlow proficiency.
• Experience with Hybrid Retrieval (FAISS, Milvus, Pinecone + BM25).
• Solid knowledge of vector databases and RAG pipelines.
• Real GenAI product experience (not academic).
• Strong English communication; ability to work with partial Poland timezone coverage