AI Knowledge Data Engineer (LLM/RAG)

TL;DR

AI Knowledge Data Engineer (LLM/RAG): Design, implement, and scale state-of-the-art AI systems combining large language models, advanced retrieval techniques, cognitive memory architectures, and knowledge representation with an accent on data pipelines, knowledge bases, and RAG workflows. Focus on orchestrating scalable training data operations, optimizing knowledge retrieval strategies, and building cognitive memory systems for AI agents.

Location: Bulgaria, Ukraine, Poland (Hybrid, Remote)

Company

Fast-growing IT and engineering nearshore provider assembling cross-border teams for global customers in fintech, travel, media, and healthcare innovation.

What you will do

• Architect, implement, and optimize RAG workflows integrating local LLMs with vector search and retrieval mechanisms (FAISS, Elasticsearch, Weaviate)
• Design and maintain scalable data pipelines for ingesting, transforming, indexing, and retrieving structured/unstructured data
• Build addressable services, tools, ontologies, knowledge graphs, and semantic models for LLMs and AI agents
• Orchestrate training data operations including curation, versioning, and lineage tracking for LLM fine-tuning
• Implement knowledge retrieval strategies, data fusion, and cognitive memory systems for persistent awareness
• Collaborate with AI researchers and engineers, evaluate new technologies, and maintain documentation

Requirements

• Bachelor’s or Master’s in Computer Science, Data Science, Machine Learning, or related
• Proven experience designing/scaling data pipelines and training workflows for LLMs/AI systems
• Strong background in information retrieval, vector search, RAG (FAISS, Pinecone, Elasticsearch, Milvus)
• Proficiency in Python and ML libraries (TensorFlow, PyTorch)
• Experience with ontologies, knowledge graphs, semantic tech (RDF, OWL, SPARQL)
• Familiarity with distributed processing/orchestration (Spark, Airflow, Kubeflow); analytical and communication skills

Nice to have

• LLM fine-tuning, prompt engineering, RAG optimization
• Data-centric AI principles and training data quality assessment
• Cloud platforms and scalable storage
• Cognitive memory architecture or AI agent design

Culture & Benefits

• Great work-life balance and competitive remuneration
• Exceptional social package, discounts, supplemental health & dental care
• Team bonding events, excellent office with relaxing/gaming areas, free bike parking & showers