Senior AI Engineer – LLM, RAG

BrightAI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. They are seeking a Senior AI Engineer – LLM, RAG to lead the development of Retrieval-Augmented Generation systems that leverage large language models and real-world knowledge sources to create intelligent assistants for industrial troubleshooting. 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 Qualifications: M.S. or Ph.D. in Computer Science, AI, Machine Learning, or a related field, with specialization in NLP or deep learning; 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 Required Skills: Machine learning, Natural language processing, Large language models, Retrieval-augmented generation, Prompt engineering, Instruction tuning, Fine-tuning models, Document retrieval, Semantic search, Embedding generation, Vector stores, Reinforcement learning, PyTorch, Hugging Face Transformers, Python programming