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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 AIPython 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.