Sushanth M R
Bengaluru,Karnataka
Work Preference
Summary
AI and Data Science Engineer with hands-on experience in building and deploying machine learning systems, real-time analytics dashboards, anomaly detection pipelines, and Generative AI applications. Skilled in complete end-to-end ML workflows including data preprocessing, feature engineering, model training, evaluation, and cloud deployment. Experienced in solving real-world industrial and enterprise problems using Python, XGBoost, LangChain, RAG pipelines, React, Docker, and REST APIs. Strong communicator and fast learner, eager to contribute to high-impact AI and ML teams.
Overview
1
1
year of professional experience
1
1
Certification
Work History
AI/ML Engineer Intern
Contriver
Bengaluru, Karnataka
01.2026 - Current
- Designed and executed complete machine learning workflows including data preprocessing, feature engineering, model training, cross-validation, and deployment-ready experimentation using Python and Scikit-learn
- Built Generative AI projects to automate practical business tasks using LangChain, open-source LLMs such as Mistral and LLaMA, and structured prompt engineering techniques
- Developed and deployed model prediction APIs using FastAPI, allowing other teams to directly use the model outputs without manual data transfer
- Wrote clear technical documentation and model evaluation reports to help the team make better data-driven decisions
- AI/ML and Generative AI Division
Data Analytics Intern
Infotact Solutions
Bengaluru, Karnataka
08.2025 - 11.2025
- Built a Real-Time Stale Bus Detector that automatically identifies ghost buses, stale GPS positions, stationary vehicles, and route deviations by reading live GTFS-Realtime data feeds from TransLink, Australia
- Designed a data ingestion and anomaly detection pipeline that continuously fetches and processes thousands of vehicle records per refresh cycle using REST-based workflows
- Built an interactive React and Leaflet map dashboard with live route filtering, color-coded issue alerts, and diagnostic tooltips - adopted by the operations team for daily monitoring
- Containerized the full platform using Docker and deployed it on Render cloud, achieving sub-minute data refresh and zero-downtime real-time operation
Education
Bachelor of Engineering - Artificial Intelligence and Data Science
SDM Institute of Technology
06.2026
Diploma - Electrical and Electronics Engineering
Government Polytechnic
01.2022
Skills
- Python
- SQL
- JavaScript
- Dart
- Scikit-learn
- XGBoost
- TensorFlow
- Predictive Analytics
- NLP
- Feature Engineering
- Model Evaluation
- LangChain
- RAG Pipelines
- FAISS
- Vector Databases
- Sentence Transformers
- Mistral
- LLaMA
- Prompt Engineering
- React
- Flutter
- Firebase
- Firestore
- Leafletjs
- Streamlit
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Power BI
- Tableau
- Docker
- Git
- GitHub
- Linux
- Flask
- FastAPI
- REST APIs
- Render
- VS Code
Certification
- Open-Source LLMs with RAG, Udemy, 2025-01-01
- ChatGPT AI Essentials, Udemy, 2025-01-01
- Python for Data Science, NPTEL, 2024-01-01
- SQL for Data Analysis, Online, 2024-01-01
Languages
Kannada
English
Hindi
Projects
- Predictive Maintenance Platform, Python, XGBoost, Streamlit, Pandas, Docker, 2026-01-01, Built a machine health monitoring platform with real-time sensor simulation, anomaly detection for vibration, pressure and temperature, failure-risk scoring, and automatic maintenance alerts. Achieved 92 percent plus fault detection accuracy using XGBoost trained on engineered time-series sensor features, estimating a 30 percent reduction in machine downtime. Added downloadable PDF diagnostic reports and a visual Streamlit dashboard showing full machine lifecycle status; containerized with Docker for easy and portable deployment.
- RAG-Based Knowledge Assistant, Python, LangChain, FAISS, Sentence Transformers, Mistral, 2026-01-01, Developed a complete Retrieval-Augmented Generation pipeline that splits documents into chunks, converts them to embeddings, stores them in FAISS for fast search, and answers user questions with full context. Improved answer relevance by 40 percent over standard LLM prompting through domain-aware retrieval and contextual re-ranking, using only open-source LLMs with no paid API cost. Delivered a clean chat interface where users upload any document and ask questions in plain English, suitable for enterprise knowledge management and internal documentation.
- Smart College Document Management App, Flutter, Firebase, Firestore, Cloud Storage, Dart, 2025-01-01, Built a secure Android and iOS mobile application for students to submit, track, and receive official college documents, with separate login roles for admins, faculty, and students. Automated document approval notifications using Firestore real-time sync, cutting processing time from days to hours and removing manual follow-ups for the college administration team.
Work Availability
monday
tuesday
wednesday
thursday
friday
saturday
sunday
morning
afternoon
evening
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Affiliations
- Institute of Electrical and Electronics Engineers
Software
Python, machine learning, Deep learning, Data science
Interests
Python, machine learning, Deep learning, Data science
Doing coding
I am an AI & Data Science graduate with experience in Python, Machine Learning, HTML, CSS, JavaScript, and SQL. I have completed projects such as Predictive Maintenance System and Social Media AI Agent using Streamlit, FastAPI, and React. I also completed an internship in AI/ML and Generative AI, where I worked on model development, data analysis, and web application development. I continuously practice coding and problem-solving to improve my technical skills
Timeline
AI/ML Engineer Intern
Contriver
01.2026 - Current
Data Analytics Intern
Infotact Solutions
08.2025 - 11.2025
Bachelor of Engineering - Artificial Intelligence and Data Science
SDM Institute of Technology
Diploma - Electrical and Electronics Engineering
Government Polytechnic