Bright AI Senior AI Engineer at Bright AI leading development of Retrieval-Augmented Generation (RAG) systems combining LLMs with external information sources. The role focuses on building AI 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.
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.
Qualification
Fluency with prompt engineeringBonus QualificationsAgentic RAG experience
Required
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).
