Read my story here:

• 🌟 Background: A seasoned software developer thriving in backend (80%) and frontend (20%) development with excellent development experience working with monolith as well as microservice architecture with languages like Python, JAVA, NodeJS and front end components like Angular building both REST APIs and GraphQL endpoints.


• ☁️ Cloud Navigator: Navigating the arena of working with cloud resources, I've deployed scalable serverless architectures using a multitude of AWS resources. Rich experience with AWS services including S3, DynamoDB, Aurora, CFT, SQS, SageMaker and Serverless Lambdas, and worked with EMR for big data processing by developing Spark jobs.


• 🗄️ Database Management: Exceptional SQL and PL/SQL querying skills and strong experience with a variety of relational databases like Oracle, MySQL, PostgreSQL, and NoSQL or non relational databases like MongoDB, DynamoDB, Redis and strong experience with ELK stack, ElasticSearch, Logstash and Kibana.


• 🤖 AI/ML Passion: From developing ML pipelines with PyTorch, TensorFlow and Keras to creating multimodal applications and predictive models, I have experience with libraries such as Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels and utilizing pretrained models through transfer learning as well as finetuning.


• ⚡ State-of-the-art AI Skills: Skilled in developing Multimodal applications using RAG techniques and Large Language Models (LLMs) with LangChain, LlamaIndex. Latest GenAI skills and fine-tuning LLMs to improve their performance in specific tasks.


• 🧩 Diverse AI Challenges: I've tackled diverse AI challenges, including NLP, NER, and data visualization, building algorithms that transform chaotic data into meaningful insights. Whether it's predicting customer happiness, optimizing talent acquisition, or developing time series models, my solutions consistently deliver.


• 🔭 Currently working on: Deploying a state-of-the-art Multimodal application powered by LangChain to generate multiple-choice questions (MCQs) using Retrieval Augmented Generation (RAG) techniques with LLMs. [Multimodal Langchain MCQ Generator](https://github.com/Charu21/MultimodalMcq)


• 📫 How to reach me: [email protected]


• 📄 Know about my experiences: https://www.linkedin.com/in/charusmitadhiman/


• 🎉 Fun fact I not only predict trends and outcomes using machine learning models but can also predict your future, thanks to my interest in astrology!



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