Gen AI Technical Lead

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.