Drive the development, implementation, and engineering of Generative AI applications leveraging the latest Large Language Models (LLMs) and advanced frameworks. This pivotal role demands hands-on proficiency in Python, cloud platforms, and cutting-edge AI techniques.

Enhance your expertise with front-end technologies, data modernization strategies, and seamless API integration. You will be instrumental in architecting and building robust, scalable, and highly efficient AI solutions from the ground up.

Spearhead the design and implementation of GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. Lead the productionization of GenAI solutions beyond proof-of-concept stages, employing scale frameworks and tools such as Pylint and Pyrit.

Develop architectures for effectively managing and processing large-scale structured and unstructured data. Create multi-modal LLM applications, incorporating text, vision, and speech capabilities. Fine-tune Small Language Models (SLMs) for domain-specific data and unique use cases.

Demonstrate deep expertise in Python programming for developing GenAI applications and automation tools. Possess strong knowledge of front-end technologies including React, Streamlit, AG Grid, and JavaScript. Showcase extensive experience with cloud platforms like Azure, GCP, and AWS for deploying and managing GenAI applications.

Master fine-tuning techniques such as PEFT, QLoRA, and LoRA. Exhibit robust understanding of LLMOps for efficient model deployment, monitoring, and management. Apply expertise in Responsible AI practices, ensuring ethical implementation and regulatory compliance. Excel in Retrieval-Augmented Generation (RAG) and Modular RAG architectures, along with data modernization strategies. Integrate APIs effectively using REST, SOAP, and other relevant protocols.