Lead the development of cutting-edge Generative AI applications, transforming concepts into robust solutions.

This senior role involves architecting and building GenAI applications from the ground up, leveraging advanced frameworks and technologies. You will be instrumental in driving innovation and delivering high-impact projects.

Focus on integrating multi-modal LLM capabilities, including text, vision, and speech models. You will also fine-tune small language models for specific industry needs, ensuring tailored performance.

Develop innovative GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.

Architect and implement solutions for large-scale structured and unstructured data handling.

Create seamless front-end integrations with React, Streamlit, and AG Grid.

Design and build data modernization and transformation pipelines.

Implement OCR and document intelligence solutions.

Utilize cloud platforms such as Azure, GCP, and AWS for application deployment.

Apply fine-tuning techniques like PEFT, QLoRA, and LoRA for LLM optimization.

Establish and manage LLMOps pipelines for efficient deployment and monitoring.

Ensure the integration of Responsible AI practices throughout the development lifecycle.

Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures.

Develop automated data curation and preprocessing tools.

Create comprehensive technical documentation for all developed applications.

Collaborate effectively with cross-functional teams to achieve project goals.

Mentor junior developers, fostering technical excellence and a culture of innovation.

A strong foundation in Python programming for developing efficient GenAI solutions is essential.

Familiarity with multi-modal LLM applications covering text chat completion, vision, and speech is required.

Expertise in cloud platforms like Azure, GCP, and AWS is necessary for deployment and management.

Experience with fine-tuning LLMs using techniques such as PEFT, QLoRA, and LoRA is crucial.

Proficiency in implementing LLMOps pipelines for CI/CD and monitoring is expected.

Knowledge of Retrieval-Augmented Generation (RAG) and Modular RAG is important.

Experience with API integration using REST, SOAP, and other protocols is needed.

Skills in Optical Character Recognition (OCR) and document intelligence are beneficial.

Ability to design and implement data modernization and transformation pipelines is key.

Demonstrated ability to guide and mentor junior developers is a significant advantage.