I am an AI/ML Consultant with extensive expertise in Generative AI, Machine Learning, Deep Learning, Large Language Models (LLMs), and Data Science. At Girikon Solutions Pvt. Ltd., I design and implement transformative solutions that address complex challenges across sectors such as transportation, energy, security, and IT.

Over the years, I have collaborated with esteemed organizations including Sumeru Software Solutions Private Limited, ClientoClarify, BroadNexus (TMTS), and Bitit. My notable projects include:

• Playground Application: Developed using our in-house Baali Model to pioneer next-generation AI solutions.
• AI ATS Application: Engineered using the Baali Model to analyze skill gaps, rank candidates, and generate tailored training programs.
• Test Case Generation SLM Model: Automated test case creation and execution powered by the Baali Model.
• Boman.ai: Contributed to a robust security platform that integrates AI/ML within DevSecOps workflows for continuous security scanning.
• BroadNexus Prajna.ai Chatbots: Developed real-time, natural conversational agents to enhance customer engagement and satisfaction.

• Core Competencies: Generative AI, Machine Learning, Deep Learning, Data Science, and Prompt Engineering.
• Advanced Tools: NLP, automated data pipelines, model evaluation, and business intelligence.
• Application Domains: Security automation, IT solutions, and data-driven enterprise strategies.

I leverage a variety of advanced frameworks and libraries to power my AI solutions. Below is a breakdown into two key categories: general frameworks for AI/DL and dedicated Generative AI (GenAI) frameworks.

• TensorFlow: A comprehensive platform for developing and deploying machine learning models with flexibility and scalability.
• PyTorch: A dynamic deep learning framework well-suited for iterative model development.
• Keras: A high-level API for building and training neural networks, typically used in conjunction with TensorFlow.
• scikit-learn: Essential for traditional machine learning tasks including classification, regression, and clustering.
• JAX: A tool for high-performance numerical computation and machine learning research.
• Fast.ai: Enables rapid prototyping and simplified training of deep learning models.
• ONNX (Open Neural Network Exchange): Facilitates interoperability among various machine learning frameworks.
• PaddlePaddle: A deep learning platform developed by Baidu for specialized applications.

Several prominent Generative AI frameworks are available for developing and deploying AI applications. These frameworks offer comprehensive tools for building applications with Large Language Models (LLMs), implementing Retrieval-Augmented Generation (RAG), and evaluating GenAI models. Key GenAI frameworks include:

• LangChain: A framework for building applications powered by language models, featuring model invocation, prompt chaining, API building, and chatbot development.
• LlamaIndex: A data framework specifically designed for building LLM applications, with a focus on RAG and agent-based solutions.
• Haystack: An end-to-end framework for building search systems and language model applications, supporting various NLP tasks and retrieval integration.
• TensorFlow: Also critical in the GenAI space for developing and deploying generative models.
• PyTorch: Popular for its dynamic computation graph, critical for iterative GenAI model development.
• LangGraph: An extension of LangChain designed for building stateful, multi-actor applications with LLMs.
• DSPy: A framework for automatic prompt tuning and optimization of LLMs.
• CrewAI: Enables the creation of role-playing AI agents to simulate complex tasks.
• AutoGen: Focused on code execution and multi-agent conversational systems.
• Microsoft Semantic Kernel: Helps integrate AI into enterprise applications seamlessly.
• RAGAS: A framework aimed at evaluating RAG pipelines.
• LiteLLM: Library for standardizing LLM usage across different providers.
• DeepEval: Focused on evaluating the performance of GenAI applications.
• Ollama: Software/python package for running local LLMs.

Other Notable Tools and Libraries:

• Hugging Face: Provides access to a vast library of pre-trained NLP models and other AI tools.
• PandasAI: Empowers Pandas DataFrames with the ability to use LLMs for enhanced data operations.
• Unsloth: A framework for fine-tuning LLMs on custom datasets.
• GraphRAG: Uses knowledge graphs to deliver advanced retrieval-augmented generation capabilities.

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This README encapsulates my professional journey, technical proficiency, and preferred tools for implementing advanced AI and deep learning solutions. It also highlights the key Generative AI frameworks that I use to build, deploy, and evaluate state-of-the-art AI applications. Feel free to connect and explore collaborative opportunities in the evolving world of AI.