Key Responsibilities :

- Design & Development : Lead the design, development, and optimization of LLM-based chatbots and AI agents.

- LangChain Implementation : Implement LangChain to build scalable and efficient AI-driven applications.

- RAG Pipeline Integration : Develop and integrate Retrieval-Augmented Generation (RAG) pipelines to enhance knowledge retrieval for chatbots.

- VectorDB Usage : Leverage VectorDB to enable efficient similarity search and retrieval of relevant data.

- AI Inference with Groq : Utilize Groq for AI inference and acceleration, ensuring optimal performance and scalability.

- LLM Fine-Tuning : Fine-tune and optimize large language models (LLMs) like GPT-4, LLama, Mistral, or

Falcon to meet specific business use cases.

- Cross-Functional Collaboration : Work closely with product managers, data scientists, and engineers to integrate AI models into applications.

- Continuous Learning : Stay abreast of advancements in AI, NLP, and machine learning to incorporate the latest technologies.

Required Skills & Qualifications :

- Proven experience in developing LLM-based chatbots and AI agents.

- Expertise in LangChain, RAG, and VectorDB technologies.

- Hands-on experience with Groq and AI acceleration techniques.

- Strong proficiency in Python and Node.js for AI/ML development.

- Experience with fine-tuning LLMs like LLama, GPT-4, Mistral, or Falcon.

- Solid understanding of machine learning, deep learning, and Natural Language Processing (NLP) techniques.

Preferred Qualifications :

- Experience with OpenAI, Hugging Face, and Anthropic APIs.

- Knowledge of prompt engineering and reinforcement learning from human feedback (RLHF).

- Prior experience in developing multimodal AI solutions (e.g., text, image, video, speech models).