Seeking a Generative AI Lead to spearhead the development of cutting-edge AI applications. This role involves building solutions from the ground up, utilizing advanced frameworks and ensuring seamless integration with modern front-end technologies.

The position emphasizes working with large-scale data, multi-modal LLMs, and implementing responsible AI practices. Cloud platform expertise and a deep understanding of LLMOps are crucial for success. You will also be instrumental in driving data modernization and transformation initiatives.

This is an opportunity to lead and mentor a team, fostering innovation in a collaborative environment. The ideal candidate will have a strong background in Python programming and a passion for pushing the boundaries of AI.

Develop and architect generative AI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Write robust and efficient Python code for AI solutions, handling large-scale structured and unstructured data. Design and implement multi-modal LLM applications, including text, vision, and speech models. Fine-tune Small Language Models (SLMs) for domain-specific applications and use cases. Integrate AI backends with user interfaces using React, Streamlit, and AG Grid. Develop data modernization and transformation pipelines to support AI functionalities. Implement solutions for Optical Character Recognition (OCR) and document intelligence. Utilize cloud platforms like Azure, GCP, and AWS for deployment and management. Apply fine-tuning techniques (PEFT, QLoRA, LoRA) to optimize LLMs and implement LLMOps. Ensure ethical AI practices and implement RAG and Modular RAG architectures. Automate data curation and preprocessing pipelines. Collaborate with cross-functional teams and mentor junior developers.

A minimum of 6 years of experience is required. Proficiency in Python programming for developing high-quality, maintainable AI code. Experience in building GenAI applications from scratch using frameworks such as Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Strong understanding of large-scale data handling, architecture design, and data modernization. Familiarity with multi-modal LLM applications (text, vision, speech) and SLM fine-tuning. Experience with front-end integration using React, Streamlit, and AG Grid. Expertise in cloud platforms including Azure, GCP, and AWS. Knowledge of fine-tuning techniques like PEFT, QLoRA, and LoRA, along with LLMOps implementation. Understanding of Retrieval-Augmented Generation (RAG) and Modular RAG. Experience with OCR and document intelligence solutions is essential. Ability to integrate APIs using REST, SOAP, and other protocols. Commitment to responsible AI practices and technical documentation. Excellent collaboration and mentorship skills are expected.