AI Engineer (RAG Specialist)

AI Engineer (RAG Specialist) We are looking for a skilled AI Engineer specializing in Retrieval-Augmented Generation (RAG) to join our team. Your primary focus will be bridging the gap between static LLMs and dynamic, proprietary data. You won't just be "calling an API"; you will be architecting the entire data lifecycle-from ingestion and chunking strategies to advanced retrieval and response synthesis. The ideal candidate understands that the secret to a great RAG system isn't just the LLM, but the quality of the retrieval and the nuances of the vector database. US Citizenship Required Key Responsibilities • Pipeline Architecture: Design and deploy end-to-end RAG pipelines using frameworks like LangChain , LlamaIndex , or Haystack . • Data Engineering: Develop robust ETL processes to ingest unstructured data (PDFs, docs, web scrapes) into high-performance vector stores. • Retrieval Optimization: Implement and tune advanced retrieval techniques, including Hybrid Search (keyword + semantic), Re-ranking (Cross-Encoders), and Parent-Document Retrieval . • Vector Database Management: Manage and scale vector databases such as Pinecone, Weaviate, Milvus, or Chroma . • Evaluation & Benchmarking: Establish rigorous evaluation frameworks (e.g., RAGAS , TruLens ) to measure faithfulness, relevancy, and hit rates. • Performance Tuning: Optimize embedding models and prompt engineering to reduce latency and "hallucinations." Technical Qualifications • Language Proficiency: Advanced Python (preferred) or TypeScript. • LLM Expertise: Hands-on experience with OpenAI GPT-4, Anthropic Claude, or open-source models like Llama 3 via Ollama or vLLM . • Vector Expertise: Deep understanding of embeddings, similarity metrics (Cosine, Euclidean), and indexing strategies (HNSW, IVF). • NLP Fundamentals: Familiarity with tokenization, context windows, and attention mechanisms. • Cloud/DevOps: Experience deploying AI applications on AWS, GCP, or Azure using Docker/Kubernetes. Preferred Skills • Experience with Agentic RAG (Multi-step reasoning and tool-use). • Knowledge of Graph Databases (Neo4j) for GraphRAG implementations. • Contributions to open-source AI projects. • Background in traditional Information Retrieval (Elasticsearch/Solr).