Introduction & Summary:We're on the hunt for a top-tier AI/ML Engineer with deep hands-on experience in building production-grade NLP, GenAI, and LLM-driven systems.If you thrive in solving complex retrieval problems, designing robust RAG architectures, and fine-tuning large-scale LLMs—this is the role built for you.This is not a research-only job. This is real engineering, building enterprise-level GenAI products that ship.12-month B2B contract (with extension) | Full-time | Remote (Preferably Poland, also open to Ukraine/Romania/CEE)Why this role is exctiting:You’ll work on the most advanced AI tech stack in production today: GraphRAG deployments at scale Hybrid Retrieval systems combining vector search + classical search Fine-tuning LLMs (GPT, LLaMA, Falcon) using LoRA/PEFT Enterprise RAG pipelines using Weaviate’s Ignition Framework Generative AI apps powering real users + real business impact A team that actually understands GenAI engineering—not just prompt tinkering Main Responsibilities: In this role, you will be responsible for developing and deploying advanced AI solutions. Architect and deploy GraphRAG systems using Neo4j + Weaviate/VectorDBs Build Hybrid Retrieval pipelines (FAISS / Milvus + BM25) Fine-tune LLMs with LoRA/PEFT for high-performance domain adaptation Create scalable GenAI workflows using LangChain, LangGraph, Hugging Face Implement and extend Weaviate’s Ignition Framework (mandatory) Deploy solutions on AWS / GCP / Azure using strong MLOps discipline Evaluate/optimize retrieval, ranking, latency, and generation quality Collaborate with engineering + product teams to build production AI systems Maintain high-quality documentation, monitoring, and model lifecycle management Key Requirements: 5–8+ years of hands-on AI/ML engineering with production deployments.

Strong NLP + LLM experience including:

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; proficiency in PyTorch/TensorFlow. 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 skills; ability to work with partial coverage of Poland's timezone. Nice to Have: Enterprise RAG & GraphRAG expertise. Vector search optimization capabilities. Knowledge graph integration experience. LLM fine-tuning and prompt engineering proficiency. Familiarity with Weaviate, Neo4j, LangChain. Experience in cloud deployment (AWS/GCP/Azure). Other Details:The position is full-time and may require partial time overlap with Poland hours. The preferred location is Poland, but candidates from Ukraine, Romania, or other CEE rcountries are also welcome to apply.