Senior AI Engineer - LLM, RAG

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 systems in production settings. Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the product roadmap. Educational Background M.S. or Ph.D. in Computer Science, AI, Machine Learning, or a related field, with specialization in NLP or deep learning. Strong research or applied background in large language models (LLMs) and retrieval-augmented generation (RAG) systems. Agentic RAG experience is highly desirable. Required Skills & Expertise 5+ years of experience in machine learning or AI with a strong focus on NLP, LLMs, or conversational AI. Fluency with modern LLMs and open-source foundational models (e.g., LLAMA, Falcon, Mistral, GPT, Claude). Experience building RAG pipelines with tools like LangChain, LlamaIndex, or custom vector database integrations, with at least one production grade system was built. Fluency with prompt engineering, instruction tuning, or fine-tuning open-source models. Deep understanding of document retrieval (semantic search, embedding generation, similarity metrics) and vector stores (e.g., FAISS, Weaviate, Pinecone). Strong foundation in core machine learning techniques, including experience with reinforcement learning (RL) or decision-making models. Proficiency with ML development frameworks such as PyTorch, Hugging Face Transformers, or similar. Strong Python programming is a must. Experience integrating AI systems into real-world applications with user-facing interfaces and operational constraints. Excellent problem-solving and critical thinking skills; ability to design solutions for complex, ambiguous problems. Strong written and verbal communication skills, with ability to collaborate cross-functionally with engineers, product managers, and domain experts. Bonus Qualifications Experience applying LLMs in industrial or physical infrastructure settings (e.g., manufacturing, logistics, utilities, energy). Knowledge of industrial control systems, maintenance workflows, or technician support processes. Exposure to multimodal models or integrating textual data with sensor and/or time-series data. Prior experience in a startup or a fast-paced environment building LLM-powered products from the ground up.