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).
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