Devang Pagare

Developing entire flask backend with multiple endpoints for Emergiq app in WSGI production server ensuring robust and scalable architecture. Initially deployed the backend using Google Kubernetes Engine (GKE), later transitioned to Digital Ocean’s App Platform for improved deployment efficiency. Actively leading the backend development and MLOps to drive the project’s success and deliver high-quality results. Engineered several prompts targeted to different modes of the app, utilizing advanced techniques to improve user interaction and experience. Developed CI/CD pipeline with GitHub Actions. Designed and implemented the app’s database schema using Firestore, optimizing for performance and scalability. Utilizing LLMs like Llama3, Mixtral, Gemma, Gemini, and APIs like Groq and Deepinfra. Creating and Managing cron jobs using Digital Ocean’s functions with triggers to automate various backend processes.

Worked in the R&D department under the Senior Vice President and developed AI solutions for the Finance sector. Used Machine learning algorithms like Regression, Random Forest, and Time Series models(ARIMA, ARMA, etc) for problems like churn prediction, demand prediction, forecasting, fraud detection, etc. Finetuned Pythia LLM on Lamini docs to improve output quality. Worked on vector databases like Qdrant, Faiss, Pinecode, and Chroma. Created the first quantized version of Llama2-7B-Finance model in GGUF format with 16bit, 32bit and 8bit precision quantizations. Developed a Q&A bot based on RAG that uses vector database and LLM to answer questions based on existing or provided knowledge base. Developed the AI-powered personal Finance Advisor that daily advises investors about their investments and suggests recommended actions. It uses LLMs, News APIs, Streamlit, Qdrant vector database, etc.

Developed a web-based application for handling and monitoring multiple client-side IoT hardware and sensors. Designed the database schema to store all the data from sensors. Implemented MQTT protocol and utilized HiveMQ as a reliable broker for seamless connectivity between software and hardware devices; reduced data transmission latency by 50% and enhanced real-time monitoring capabilities. Used the simulator to verify software integrity and connectivity. Created/Updated and implemented protocol and payload guide.