GenAI Technical Lead

GenAI Technical Lead_Full-time_Bangalore/Pune/Navi Mumbai/Noida/Hyderabad/Chennai

Job Title : GenAI Technical Lead

Experience Required : 8-10 Years

Location : Bangalore/Pune/Navi Mumbai/Noida/Hyderabad/Chennai

Employment Type : Full-time

Primary Skills: Gen Ai, Azure Open Ai, Python and which are mentions in cheat sheet

Job 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.