Generative AI Developer

Area(s) of responsibility Job Title: GEN AI Developer Location - Noida/HYD/Bengaluru/Pune/Chennai/Mumbai

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.

Python Programming: Deep expertise in Python for building GenAI applications and automation tools. Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain. 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. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc. Anti-hallucination and anti-gibberish tools such as Bleu etc. Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development. Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. (any two cloud exp.) Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine) LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations. RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures. Data Modernization: Expertise in modernizing and transforming data for GenAI applications. OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools. API Integration: Experience with REST, SOAP, and other protocols for API integration. Data Curation: Expertise in building automated data curation and preprocessing pipelines. Technical Documentation: Ability to create clear and comprehensive technical documentation. Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.