NLP & LLM Engineering with RAG

Tahmid Rahman is a highly respected AI practitioner and mentor, known for building production-grade machine learning systems and solving complex real-world problems across multiple industries. As the AI Team Lead at iBusinessFormula and former AI Engineer at Brain Station 23, he brings a rare blend of technical depth, leadership, and practical field experience—making him uniquely qualified to guide students through modern AI engineering.

Professional Experience

AI Team Lead at iBusinessFormula

Tahmid leads end-to-end AI solution development, from problem discovery to deployment. He oversees model architecture design, optimization, prompt engineering workflows, LLM integration, and scalable MLOps pipelines. His leadership ensures that AI systems not only work in theory but operate reliably in production environments. He regularly mentors junior engineers, conducts code reviews, and drives innovation within the AI team.

Software Engineer at Brain Station 23

At Brain Station 23, Tahmid plays a central role in developing AI-powered systems with a sharp focus on practical impact. His work revolves around designing AI agents that simulate human-level reasoning, solve user-specific tasks, and automate client workflows using advanced techniques.

His core responsibilities include:

• Designing and deploying AI agents that handle dynamic user interactions, adapt over time, and intelligently make decisions based on context and intent.

• Leading LLM integration pipelines (like OpenAI, Cohere, or custom-trained models) with LangChain, RAG (Retrieval-Augmented Generation), and memory-enabled agents.

• Building tools for autonomous agents that carry out sequential tasks, connect to APIs, retrieve documents, extract structured data, and provide accurate, context-aware responses.

• Collaborating with cross-functional teams to translate client needs into intelligent assistant systems that improve support, analytics, and automation.

He consistently solves enterprise-grade AI challenges—from prompt-tuning for reliability to implementing long-term memory in conversational agents.

Tahmid’s AI journey is not confined to labs or theory—he builds real-world, production-ready agents that are being used by companies to automate processes, enhance customer service, and extract insights from unstructured data.

Whether it's an LLM-powered customer support bot, a document retrieval and summarization assistant, or a task automation agent with multi-step reasoning, Tahmid has solved these problems hands-on.

He doesn't just teach how agents work—he teaches how to make them useful, efficient, and scalable.

Students under his guidance will learn:

• How to go from zero to building deployable AI agents

• How to architect systems with tools like LangChain, vector databases, and RAG

• How to ensure reliability, reduce hallucination, and scale agent performance

With a problem-solving mindset, solid teamwork, and deep AI experience, Tahmid is not just a teacher—he’s a technical leader who will help students confidently build the future of autonomous AI systems.