Join our innovative team as a Generative AI Engineer, where you'll pioneer intelligent applications. This role focuses on integrating cutting-edge Generative AI, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), with intuitive user experiences. You will lead the development of agentic applications and responsive frontend interfaces.

We are looking for a creative and experienced individual to drive advancements in AI-powered solutions. Your work will directly impact how users interact with our intelligent systems, making complex technology accessible and user-friendly.

Key responsibilities include designing and implementing Generative AI solutions using prominent LLMs like GPT, Claude, and LLaMA. You will develop and optimize RAG pipelines, leveraging vector databases such as ChromaDB for local work and Databricks Mosaic ML Vector Stores for production. Building and maintaining agentic workflows and conversational AI agents using frameworks like LangGraph and OpenAI is also a core part of this role.

Additional duties involve performing vectorization with models like GTE-large and BGE, with opportunities for fine-tuning to enhance accuracy. You will collaborate closely with backend and app development teams to seamlessly integrate Generative AI capabilities into our product offerings, ensuring a cohesive and powerful user experience.

A deep understanding of LLMs, prompt engineering, and transformer architectures is essential for this position. Candidates must possess hands-on experience with RAG architectures, vector search, and embedding models. Proficiency in Python is a must, along with experience in libraries like LangChain, Transformers, OpenAI SDK, or Hugging Face.

Familiarity with agentic frameworks such as AutoGen, OpenAI, CrewAI, or LangGraph is required. You should also have experience deploying AI models through APIs or microservices. A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field is preferred, along with experience in cloud platforms (AWS, GCP, Azure) and CI/CD pipelines.