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👋 Hi, I'm Abhijith

🚀 AI/ML Engineer | 🧠 Generative AI & RAG Systems | 🧰 MLOps & Cloud Infrastructure | 🧪 Product Builder

💼 Who Am I?

I'm Full stack AI/ML engineer with a passion for building products that leverage cloud infrastructure, machine learning & AI end to end Data pipelines. My work spans across technical and non-technical steakholders generative AI assistants, multi-modal LLMs, time-series forecasting, and real-time meeting intelligence using tools like **AWS SageMaker**, **Jenkins**, **Docker**, and **Recall.ai**.

Currently, I’m building:

• 🗣️ An AI assistant for meetings that processes live transcripts, researches facts vs current transcripts, and summarizes in real-time.
• ✍️ AutoCover – A resume & cover letter generator powered by open-source LLMs, RAG pipelines (FAISS + Neo4j), and React/FastAPI
• Buidling an MCP for stock technical insights and news - Creating MCP server for personal usage to get stock technical insights + news.
• Sample RAG implementation - Sample implementation of RAG pipeline utilizing OpenAI embeddings + LLM Calls + Prompt Engineering + Pinecone vector database.

Previous Projects:

• Big Data Series on LinkedIn - Did a Spark/Streaming big data series, showing spark fundamentals and streaming fundamentals. For fun!!
• End to end MLOps Pipeline - Utilized Jenkins and several AWS services to build end to end MLOps pipeline serving, over 20+ models run through Sagemaker pipelines, each model output stored in AWS S3, Model registry to track models and utilized canary deployment for models utilzing AWS endpoints and ensuring it is scaled accounting for varying traffic. Terraform was utilized to deploy IaC. Finally, the end to end system is monitored using AWS CloudWatch + Prometheus + Graphana - Connected with SNS, so the developers are notified if systems were ever to go down.
• Image Classification using AWS SageMaker - Use AWS Sagemaker to train a pretrained model that can perform image classification by using the Sagemaker profiling, debugger, hyperparameter tuning and other good ML engineering practices. This can be done on either the provided dog breed classication data set or one of your choice.
• Stock Market Analysis and Prediction - This project aims to leverage historical stock data from companies like Google, Amazon, Microsoft and Apple alongside financial indicators, to forecast future stock prices.
• Data Science Projects - A list of various data science and data engineering projects in AWS and private enviornments, showcasing different skills like ETL, feature engineering, model tuning etc.

⚒️ Tech Stack

📦 Projects

⚙️ Image Classification using AWS SageMaker
Use AWS Sagemaker to train a pretrained model that can perform image classification by using the Sagemaker profiling, debugger, hyperparameter tuning and other good ML engineering practices. → AWS + Sagemaker + Computer Vision + OpenCV + Pretrained models + Hyperparameter tuning

🚧 MCP Server for Swing Trading Data Retrieval Build an MCP server that standardizes access to real-time and historical stock data for swing trading, enabling agents to query market data, technical indicators, volume trends, and intraday price ranges through a unified tool interface while following reliable data engineering practices. → MCP + Market Data APIs + Time-Series Processing + Technical Indicators + Caching + Observability

⚙️ Stock Prediction Model
This project aims to leverage historical stock data from companies like Google, Amazon, Microsoft and Apple alongside financial indicators, to forecast future stock prices. → Unit tests | PySpark | Redshift | Monitoring with CloudWatch | Streamlit

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📫 Connect With Me

(https://linkedin.com/in/abhijithvamadev)

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💡 “Building intelligent systems that not only learn, but serve.”