BrightAI builds physical AI systems and an end-to-end Stateful platform that ingests sensor, drone, and wearable data to monitor and predict failures in infrastructure assets across multiple verticals.
<h1>Senior AI Engineer – RAG Systems</h1> <p><strong>Bright.AI</strong> 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.</p> <p>We are now hiring a <strong>Senior AI Engineer – LLM, RAG</strong> 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.</p> <p>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.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li>Lead the architecture and development of RAG systems that combine LLMs (e.g., LLAMA, Mistral, Claude, GPT) with structured and unstructured external information sources.</li> <li>Develop AI-powered assistants to support technicians in diagnosing and resolving anomalies or failures in factory, plant, or industrial settings.</li> <li>Build pipelines to ingest, preprocess, and index large corpora of documents (manuals, logs, notes, procedures) for semantic search and grounding.</li> <li>Customize and fine-tune foundational models to incorporate domain-specific language, tone, and logic for industrial troubleshooting scenarios.</li> <li>Collaborate with product, data, and cloud teams to design scalable, privacy-compliant, and latency-sensitive LLM applications.</li> <li>Design evaluation strategies to measure performance, accuracy, and user experience of RAG-enabled systems in production settings.</li> <li>Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the product roadmap.</li> </ul> <h3><strong>Educational Background</strong></h3> <ul> <li>M.S. or Ph.D. in Computer Science, AI, Machine Learning, or a related field, with specialization in NLP or deep learning.</li> <li>Strong research or applied background in large language models (LLMs) and retrieval-augmented generation (RAG) systems. Agentic RAG experience is highly desirable.</li> </ul> <h3><strong>Required Skills & Expertise</strong></h3> <ul> <li>5+ years of experience in machine learning or AI with a strong focus on NLP, LLMs, or conversational AI.</li> <li>Fluency with modern LLMs and open-source foundational models (e.g., LLAMA, Falcon, Mistral, GPT, Claude).</li> <li>Experience building RAG pipelines with tools like LangChain, LlamaIndex, or custom vector database integrations, with at least one production grade system was built.</li> <li>Fluency with prompt engineering, instruction tuning, or fine-tuning open-source models.</li> <li>Deep understanding of document retrieval (semantic search, embedding generation, similarity metrics) and vector stores (e.g., FAISS, Weaviate, Pinecone).</li> <li>Strong foundation in core machine learning techniques, including experience with reinforcement learning (RL) or decision-making models.</li> <li>Proficiency with ML development frameworks such as PyTorch, Hugging Face Transformers, or similar. Strong Python programming is a must.</li> <li>Experience integrating AI systems into real-world applications with user-facing interfaces and operational constraints.</li> <li>Excellent problem-solving and critical thinking skills; ability to design solutions for complex, ambiguous problems.</li> <li>Strong written and verbal communication skills, with ability to collaborate cross-functionally with engineers, product managers, and domain experts.</li> </ul> <h3><strong>Bonus Qualifications</strong></h3> <ul> <li>Experience applying LLMs in industrial or physical infrastructure settings (e.g., manufacturing, logistics, utilities, energy).</li> <li>Knowledge of industrial control systems, maintenance workflows, or technician support processes.</li> <li>Exposure to multimodal models or integrating textual data with sensor and/or time-series data.</li> <li>Prior experience in a startup or a fast-paced environment building LLM-powered products from the ground up.</li> </ul>