Gen AI Technical Lead

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 Autogen, 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. 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: Leverage Azure, GCP, and AWS for deploying and managing 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. 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,Pyritetc. 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. 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.