Generative AI Lead

Long Description Area(s) of responsibility

Job Title: GEN AI Lead Location - Noida/HYD/Bengaluru/Pune/Chennai/Mumbai Experience Required - 6+ years Only

• Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
• Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
• Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
• Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
• Fine-tune SLM(Small Language Model) for domain specific data and use cases.
• Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.
• Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
• OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools.
• API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery.
• Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications.
• Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.
• LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.
• Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.
• RAG and Modular RAG: Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance.
• Data Curation Automation: Build tools and pipelines for automated data curation and preprocessing.
• Technical Documentation: Create detailed technical documentation for developed applications and processes.
• Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions.
• Mentorship: Guide and mentor junior developers, fostering a culture of technical excellence and innovation.