Generative AI Developer

Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), 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.
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.
innovation.