Senior AI Engineer – LLM, RAG

Sr. AI Engineer – LLM RAG BrightAI 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 Sr. 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.