Architect, implement, and optimize retrieval-augmented generation (RAG) workflows by integrating local LLMs (e.g., Llama) with retrieval mechanisms (vector search, Elasticsearch, FAISS, Weaviate)
Design, build, and maintain scalable data pipelines for ingesting, transforming, indexing, and retrieving structured and unstructured data from diverse sources
Collaborate with AI researchers, data scientists, and engineers to align knowledge architecture with business objectives and ensure data quality
Evaluate and integrate new technologies and research advancements in LLMs, RAG, information retrieval, and knowledge representation
Develop and maintain ontologies, knowledge graphs, and semantic data models to structure and integrate domain-specific knowledge for improved retrieval and reasoning

Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field
Proven experience designing and scaling data pipelines and training data workflows for LLMs or similar AI systems
Strong background in information retrieval systems, vector search technologies, and RAG frameworks (e.g., FAISS, Pinecone, Elasticsearch, Milvus)
Proficiency in programming (Python) and machine learning libraries (TensorFlow, PyTorch)
Experience with ontologies, knowledge graphs, and semantic technologies (RDF, OWL, SPARQL)
Familiarity with distributed data processing and orchestration tools (e.g., Spark, Airflow, Kubeflow)
Excellent analytical, problem-solving, and communication skills
Ability to work collaboratively in a cross-functional, fast-paced environment

AI Knowledge Data Engineer

Key skills