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Job Application for Senior AI Engineer – LLM, RAG

BrightAI Corporation

San Francisco, CAOn-siteFull-time$180k – $280k / year

About this role

Job Application for Senior AI Engineer – LLM, RAG at BrightAI Corporation Senior AI Engineer – LLM, RAG Palo Alto, CA Apply Senior AI Engineer – RAG Systems Bright.AI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our AI platform processes visual, spatial, and temporal data from billions of real-world events—captured across edge devices, mobile sensors, and cloud infrastructure—to enable intelligent decision-making at scale. We are now hiring a Senior AI Engineer – LLM, RAG to lead the development of Retrieval-Augmented Generation (RAG) systems that harness the power of large language models (LLMs) and real-world knowledge sources. This role is pivotal to building next-generation intelligent assistants that help technicians and operators troubleshoot complex issues in industrial settings. You’ll work at the intersection of NLP, foundational models, and real-time information systems—developing intelligent tools that turn manuals, technician notes, and sensor data into actionable, conversational guidance for the physical world. Responsibilities - Lead the architecture and development of RAG systems that combine LLMs (e.g., LLAMA, Mistral, Claude, GPT) with structured and unstructured external information sources. - Develop AI-powered assistants to support technicians in diagnosing and resolving anomalies or failures in factory, plant, or industrial settings. - Build pipelines to ingest, preprocess, and index large corpora of documents (manuals, logs, notes, procedures) for semantic search and grounding. - Customize and fine-tune foundational models to incorporate domain-specific language, tone, and logic for industrial troubleshooting scenarios. - Collaborate with product, data, and cloud teams to design scalable, privacy-compliant, and latency-sensitive LLM applications. - Design evaluation strategies to measure performance, accuracy, and user experience of RAG-enabled

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