GenAI Technical Lead

GenAI Technical Lead_Full-time_Bangalore/Pune/Navi Mumbai/Noida/Hyderabad/ChennaiJob Title: GenAI Technical LeadExperience Required: 8-10 YearsLocation: Bangalore/Pune/Navi Mumbai/Noida/Hyderabad/ChennaiEmployment Type: Full-timePrimary Skills: Gen Ai, Azure Open Ai, Python and which are mentions in cheat sheetJob Description:The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions.Key Responsibilities:- Application Development: Build GenAI applications from scratch using frameworks like Langchain, LangGraph and LlamaIndex.- Agentic AI framework: Practical knowledge of Multi agentic AI frameworks like Langgraph, Autogen and Crew AI- 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.- 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: Design and deploy scalable AI solutions leveraging a comprehensive suite of Azure AI services. Knowledge of AWS for deploying and managing GenAI applications is an added advantage.- 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.Required Skills :- 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 and Agentic 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 and Agentic AI applications.- Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.- 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.- Mentorship: Proven ability to mentor junior developers and foster a culture of technical excellence.