01Overview
Role & responsibilities
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 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. (any two cloud exp.)
Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine)
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. .
