Qureos Mentor

NexOne, Inc.

<div>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 &quot;calling an API&quot;; 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<ul><li>Pipeline Architecture: Design and deploy end-to-end RAG pipelines using frameworks like LangChain, LlamaIndex, or Haystack.</li><li>Data Engineering: Develop robust ETL processes to ingest unstructured data (PDFs, docs, web scrapes) into high-performance vector stores.</li><li>Retrieval Optimization: Implement and tune advanced retrieval techniques, including Hybrid Search (keyword + semantic), Re-ranking (Cross-Encoders), and Parent-Document Retrieval.</li><li>Vector Database Management: Manage and scale vector databases such as Pinecone, Weaviate, Milvus, or Chroma.</li><li>Evaluation &amp; Benchmarking: Establish rigorous evaluation frameworks (e.g., RAGAS, TruLens) to measure faithfulness, relevancy, and hit rates.</li><li>Performance Tuning: Optimize embedding models and prompt engineering to reduce latency and &quot;hallucinations.&quot;</li></ul> 
Technical Qualifications<ul><li>Language Proficiency: Advanced Python (preferred) or TypeScript.</li><li>LLM Expertise: Hands-on experience with OpenAI GPT-4, Anthropic Claude, or open-source models like Llama 3 via Ollama or vLLM.</li><li>Vector Expertise: Deep understanding of embeddings, similarity metrics (Cosine, Euclidean), and indexing strategies (HNSW, IVF).</li><li>NLP Fundamentals: Familiarity with tokenization, context windows, and attention mechanisms.</li><li>Cloud/DevOps: Experience deploying AI applications on AWS, GCP, or Azure using Docker/Kubernetes.</li></ul> 
Preferred Skills<ul><li>Experience with Agentic RAG (Multi-step reasoning and tool-use).</li><li>Knowledge of Graph Databases (Neo4j) for GraphRAG implementations.</li><li>Contributions to open-source AI projects.</li><li>Background in traditional Information Retrieval (Elasticsearch/Solr).</li></ul></div>

AI Engineer (RAG Specialist)

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NexOne, Inc.

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AI Engineer (RAG Specialist)