5+
Years Experience
20+
Projects Completed
1.5M+
Data Processed
10+
Happy Clients
01About Me
I am a graduate from Northeastern University who loves transforming complex data challenges into GenAI breakthroughs. Expert in LLM fine-tuning, RAG architectures, and prompt engineering with a powerful foundation in HPC and data pipelines.
Consistently delivering production-ready AI systems that drive real business impact, I combine technical expertise with business acumen to create solutions that matter.
Professional Highlights
- Developed RAG-driven chatbots reducing troubleshooting tickets by 40%
- Enhanced HPC reliability by analyzing 2.5M SLURM jobs using PySpark
- Implemented Docker containers with Azure Kubernetes Service
- Managed data processing for major clients including Apple, M-Benz, and Google
- Led pre-processing for voice recognition ML models, boosting accuracy by 18%
02Technical Skills
Programming & Scripting
- Python
- SQL
- Bash
- Java
Data Engineering & Big Data
- Apache Spark
- Apache Kafka
- Hive
Databases & Data Warehousing
- MySQL
- PostgreSQL
- MongoDB
- Snowflake
- Databricks
- BigQuery
Cloud & DevOps
- Azure
- Docker
- Kubernetes
- GCP
- CI/CD
ETL/Workflow Orchestration
- Apache Airflow
- MLflow
- Talend
- Informatica
Analytics Tools
- Pandas
- NumPy
- Jupyter
- Tableau
- PowerBI
Data Extraction
- BS4
- crawler4ai
- Selenium
- Scrapy
Machine Learning & AI
- TensorFlow
- RAG
- LLM Fine-tuning
- MLops
- Agentic AI modeling
- Prompt Engineering
- Deep Learning & Neural Networks
03Professional Experience
Graduate Research Assistant
Northeastern University | Boston, MA, USA
- I landed an absolute gem of an opportunity with Northeastern University’s Research Computing team—a crew of downright tech sorcerers! We were the masterminds behind the HPC cluster and cloud storage, basically running an in-house AWS for NU. Picture this: a high-octane squad keeping the university’s research engine roaring with cutting-edge computing power. My mission? Crafting custom containers and software from scratch—building the tools that made science happen. But that’s not all—I was the go-to troubleshooter, swooping in to solve the wildest tech puzzles for scientists and researchers. Working alongside brilliant minds every day? It was like living in a sci-fi blockbuster, and I loved every second of it!
Software Engineer II
Value Labs solutions | Hyderabad, Telangana, India
- Then came the plot twist! My team couldn’t get enough of my hustle and passion, and within just one year—a rare feat in our org—they handed me the keys to the Senior Software Engineer kingdom. I leveled up to tackle data integration and migration, weaving together systems like a tech wizard. I kept a sharp eye on DevOps, making sure the pipeline hummed, while jumping in to squash bugs flagged by the testing crew. Debugging? Data flows? DevOps monitoring? I was the Swiss Army knife they didn’t know they needed!
Software Engineer I
Value Labs solutions | Hyderabad, TG, India
- I kicked off my career with an electrifying team of engineers who set the stage for something epic. As an SDET, I dove headfirst into automation testing, crafting a rock-solid test framework for an ERP system that could handle anything we threw at it. I owned the testing game and nailed pre-prod stage deployments like a pro—think of me as the gatekeeper ensuring everything ran smoothly before the spotlight hit.
Co-Founder
4-Tech AI&ML Solutions | Bengaluru, KA, India
- What started as a humble college side hustle for some extra pocket money quickly snowballed into something extraordinary.Fueled by an obsession with top-notch quality, this small gig caught the eye of industry titans. Before long, we were rubbing shoulders with the likes of Google, Mercedes-Benz, Apple, and TellUS Appen—pretty wild for a crew of campus dreamers! We dove headfirst into some seriously cool projects: teaching Siri and Google Assistant to master Indic languages, turbocharging Google Maps search to pinpoint accuracy, fine-tuning audio-to-text models that hear every whisper, and even sharpening the ADAS systems powering Benz’s cutting-edge rides. From tinkering for fun to shaping the tech that runs the world, it’s been one heck of a ride
04Featured Projects
GenBI- Agentic AI for Business Intelligence
February 2025 - March 2025
Developed Streamlit app with custom agentic AI for intuitive data analysis and visualization. Deployed OpenAI GPT-driven agents for classifying queries, manipulating data, and generating visualizations.
RAG Chat Bot for Research Computing Documentation
August 2024 - October 2024
Developed a conversational AI chatbot leveraging Retrieval-Augmented Generation (RAG) for accessing research documents. Combined Azure, Airflow, and MLflow for data collection, automated pipeline operations, and model version control.
Resume Analyzer and Customized Job searching Ai
January 2025 - January 2025
Resume Analyzer is an interactive Streamlit application designed to help job seekers analyze their resumes and find relevant job opportunities on LinkedIn. By uploading a PDF resume, the app extracts key information, identifies relevant keywords, and uses them to search for job postings based on experience level and posting date. The results are presented in a visually appealing, interactive table with clickable links to job postings.
Real-TimePlateNumberDetection
November 2024 - November 2024
Detect license plates from video streams or images, Segment individual characters from the detected plate. Recognize the characters using a trained SVM model, Output the complete license plate number
CNN based Road Sign Recognition
January 2024 - January 2024
Developed a CNN-based AI system for accurate road sign recognition using Python and TensorFlow. Employed data augmentation techniques to improve model generalization and simulate real-world conditions.
Weather Forecasting Model Using LSTMs
December 2025 - December 2025
This project aims to develop a robust weather forecasting model using Long Short-Term Memory (LSTM) neural networks. These neural networks can process sequential data and excel in capturing long-term dependencies, making them particularly suitable for weather forecasting tasks. The model uses historical weather data from NOAA’s National Centers for Environmental Information (NCEI) to predict critical weather parameters such as temperature, dew point, and wind speed. The project includes data preprocessing, model building, and optimization through hyperparameter tuning and performance evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE). This study sheds light on the potential of Deep Learning techniques in weather forecasting, offering a useful tool for making accurate weather predictions and ultimately driving data-driven decision - making across industries.
05Education & Certifications
MS in Data Analytics Engineering
Northeastern University, Boston
Relevant Coursework:
B'Tec in Electrical and Electronics Engineering
REVA University, Bengaluru