I'm a B.E. Automobile Engineering student focused on Applied AI & Robotics at Easwari Engineering College, Chennai, India. My interests span artificial intelligence, machine learning, agentic systems, computer vision, robotics, data science, and intelligent developer tools.

I enjoy exploring how AI can solve practical problems, automate complex workflows, improve engineering systems, and support research. My long-term goal is to become an AI Research Engineer, combining strong software engineering fundamentals, mathematical understanding, and reproducible experimentation.

• 🔭 Exploring AI agents, multi-agent systems, machine learning, and AI-powered developer tools.
• 🧪 Participating in student research and college laboratory projects.
• 🤖 Building projects across AI, computer vision, analytics, reinforcement learning, and engineering simulation.
• 🌕 Interested in lunar image registration, aerospace applications, and space technology.
• 📚 Preparing for GATE DA 2027, targeting a strong rank.
• 🌱 Continuously developing my skills in deep learning, AI systems, and research methodology.
• 📍 Chennai, Tamil Nadu, India.
• 🗣️ Languages: English and Tamil.

Current focus: Applied AI Engineering · Agentic AI · Research-Oriented Projects · Software Engineering Fundamentals

• GitHub: @Giridharan1718
• LinkedIn: giridharan-g
• Portfolio repository: my-portfolio

B.E. Automobile Engineering — Applied AI & Robotics focus

Easwari Engineering College, Chennai, India

• Exploring the intersection of engineering, AI, robotics, and intelligent systems.
• Developing practical programming, machine-learning, and analytical skills.
• Participating in student research and college laboratory projects.

Expected graduation date is omitted here because the available profile records contain conflicting dates.

Machine Learning Intern — Cognifyz

Reported areas of work:

• Data preprocessing and feature engineering.
• Machine-learning model training and evaluation.
• Python, Pandas, and Scikit-learn workflows.
• Documentation, troubleshooting, and deployment support.

AI & Software Systems Intern — Ragas Aerospace

Reported areas of exposure:

• AI and software development.
• Aerospace and UAV-related applications.
• Testing, optimization, automation, and documentation.

AI Intern — AxonCore Technologies

Reported areas of exposure:

• Python-based AI/ML development.
• Data preprocessing and experimentation.
• Model evaluation and practical AI applications.

Note: Verify the exact legal employer name, dates, titles, and deliverables before using these entries in formal applications. The available records contain an employer-name discrepancy for the machine-learning internship.

Student Research Member — Student Research and Development Cell (SRDC)

Easwari Engineering College

• Participation in student research initiatives.
• Faculty and student collaboration.
• Research-oriented technical activities and workshops.

College Research Laboratory

• Working on research projects under faculty/mentor guidance.
• Exploring technical problem-solving and experimental approaches.

Research project titles, publications, and final findings should be added only when documented.

• Tamizhan Skills RISE (NSDC): Reported learning across cloud computing, AI/ML, deep learning, and practical technical projects.
• JPMorgan Chase Software Engineering — Forage: Virtual experience.
• Tata iQ GenAI Analytics — Forage: Virtual experience.
• AWS Solutions Architecture — Forage: Virtual experience.
• Deloitte Cybersecurity — Forage: Virtual experience.
• Google Cloud Gen AI Academy APAC Edition: Completion reported.

Training programs and virtual experiences are not represented as full-time employment.

The projects below represent a mix of software repositories, implemented projects, prototypes, research directions, and concepts. Their maturity varies; descriptions should be updated as implementation and testing progress.

These project names were supplied in the profile information. Repository links should be checked whenever a project is renamed or moved.

Multi-Agent Scam and Fraud Prevention

• Focus: Exploring multi-agent AI for scam and fraud detection/prevention.
• Technologies reported: Python, Google ADK, Gemini.
• Associated achievement: Reported Global Top 4 at UWA Hack for Impact 2026.
• Next priorities: Document agent roles, tool interfaces, datasets, evaluation, limitations, and reproducibility.

Multi-Agent AI Platform

• Agent workflow patterns: sequential, parallel, router, and loop.
• Concepts: Tool calling, sessions, memory, and orchestration.
• Example applications mentioned: Weather agent and trip planner.
• Technologies reported: Python, Google ADK, Gemini, and FastAPI.

RAG AI Assistant

• Focus: Retrieval-augmented generation and knowledge-grounded responses.
• Technologies mentioned: LLMs, embeddings, vector search, and FastAPI.
• Future priorities: Retrieval evaluation, source attribution, hallucination testing, and latency measurement.

Brochure Auditor

• Focus: AI-assisted document review using a multi-agent workflow.
• Technology mentioned: Google ADK.

AI Workforce Lite

• Focus: Productivity-assistant concept and AI-enabled workflows.

OpenEnv Email Triage and Response

• Focus: Email classification and AI-assisted response workflows.

TOTALSA Vehicle-Sales Forecasting

• Focus: Time-series forecasting.
• Methods explored: ARIMA, SARIMA, Prophet, and LSTM.
• Previously reported LSTM metrics: MAE 0.69, RMSE 1.18, and MAPE 4.76%.
• Validation status: Dataset configuration, evaluation procedure, and reported metrics require verification before being treated as final results.

Restaurant Analytics & ML

• Focus: Restaurant intelligence, prediction, recommendation, and geospatial analysis.
• Reported dataset size: 9,551 restaurants.
• Tasks explored: Rating prediction, restaurant recommendations, cuisine classification, and geographic visualization.
• Technologies reported: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly, Folium, and Jupyter.

SMS Spam/Ham Classification

• Focus: Classifying SMS messages as spam or legitimate.
• Methods mentioned: CountVectorizer and/or TF-IDF, Logistic Regression, and Naive Bayes.
• Dataset mentioned: SMS Spam Collection.
• Reported accuracy has varied across prior records and should be confirmed using the actual evaluation results.

Car Price Prediction

• Focus: Regression and feature engineering for vehicle-price estimation.
• Recommended reporting: MAE, RMSE, and R² rather than an undefined regression “accuracy” percentage.

Movie Recommendation System

• Focus: Collaborative filtering and movie recommendations.
• Dataset mentioned: MovieLens.
• Evaluation protocol and exact dataset version require confirmation.

AI Bias Detector

• Focus: Responsible AI and potential bias-detection workflows.
• Research directions: Fairness metrics, bias analysis, evaluation, and mitigation.

Driver Drowsiness Detection

• Focus: Detecting possible driver drowsiness and issuing alerts.
• Technologies mentioned: Python, OpenCV, dlib and/or MediaPipe, and NumPy.
• Techniques: Facial landmarks, eye-aspect ratio (EAR), mouth-aspect ratio (MAR), eye-closure detection, and yawning detection.

YOLO Object Detection

• Focus: Object detection using YOLO and OpenCV.
• Next priorities: Record the model version, dataset, classes, mAP, precision/recall, and inference speed.

Autonomous Lane Keeping

• Focus: Lane-keeping and steering-control logic.
• Methods mentioned: Kinematic bicycle model and lane/steering logic.
• Environment mentioned: Python and Google Colab.

Cyber Secure Chat

• Focus: Secure messaging.
• Security claims should be supported by documentation of cryptographic design, key management, and testing.

Trailer Sway Reduction

• Focus: Reducing trailer sway using a simplified feedback-control simulation.
• Tool: MATLAB.
• Approach: PD feedback control.
• Constraints: No 3D modeling; Simulink optional.
• Previously reported results: 35.07% peak reduction and 6.30-second settling time.
• Status: These figures are provisional and need reproducible test conditions and baseline comparisons.

Quarter-Car Suspension Simulation

• Focus: Vehicle-suspension modeling.
• Tools mentioned: MATLAB and/or Simulink.

LUNAR_AI / LunaPrint-X / LunarAI

• Focus: Lunar image matching and registration, including a Chandrayaan-2-related problem.
• Problem identifier associated with LunaPrint-X: SIH26166.
• Methods proposed or mentioned:
  • FAISS.
  • SuperPoint.
  • LightGlue.
  • MAGSAC++ verification.
  • Streamlit.
• Development notes mention IIRC, OHRC, TMC, and CSV files.
• Status: The available records do not establish whether these names represent separate projects or different stages of the same work. Implementation, matching accuracy, registration success rate, and submission outcome need confirmation.

Instagram Clone + AI Chatbot

• Technologies mentioned: React, Node.js, Express, MongoDB, Python, FastAPI, and JWT.
• Reported historical metrics require verification before being used as performance claims.

Hospital Booking AI System

• Focus: Appointment booking, doctor/slot management, cancellation, dashboards, natural-language booking, and a smart waitlist.
• Proposed technologies: React, Tailwind CSS, FastAPI, PostgreSQL, and Docker.
• Status: Implementation and deployment details require confirmation.

Flask CI/CD Demo

• Focus: Automated testing and deployment workflow.
• Proposed technologies: Flask, Docker, GitHub Actions, and AWS EC2.
• Intended pipeline: Linting, testing, building, publishing, and deployment.

VoiceLens AI

• Focus: Android accessibility concept involving live subtitles, text-to-speech, continuous reading, a floating player, voice selection, speed, and pitch.
• Proposed technologies: Kotlin, Jetpack Compose, and Material 3.

ProofChain AI

• Focus: Digital trust and verification concept.

• OpenEnv RL Environment: Custom reinforcement-learning environment, tasks, and reward shaping; a Hugging Face Spaces deployment was reported.
• Algorithmic Trading / IMC Prosperity: Competition-related work; strategy and performance results are not established.
• Python Data Analysis: A proposed data-analysis project; final completion status is unknown.
• AI Chatbot: NLP and intent-classification project; implementation details require confirmation.
• Small analytics projects: Animal Word Trends, Warm Waters Peru, Anchoveta Fish Catch, Tusked Elephants, Top Songs Analysis, Coffee Survey, Flight Delays, Coral Conundrum, Deepest Divers, Lion Attacks, Primate Research, Silent Spring, Skeletal Variation, Stopping Distances, Cicadas Are Coming, Granola Health, Mondrian Art, How Couples Met, and Any Animal Except Project.

• UWA Hack for Impact 2026: Reported Global Top 4 for a multi-agent scam and fraud prevention project.
• OpenEnv RL Hackathon: Reported solo participation with a reinforcement-learning project.
• Smart India Hackathon (SIH): Participant.
• TenzorX National AI Hackathon: Participation reported.
• IMC Prosperity: Algorithmic-trading challenge participation reported.
• TCS CodeVita Season 13: Reported global rank 15,597; verify the certificate before publishing.
• Mechathon 2K26: Participation and award recognition reported; exact category requires confirmation.
• HackerRank: Silver badge and three-star status reported.
• LeetCode: 150+ problems solved in this profile; update to the current verified count.

Participation, awards, rankings, and performance claims should be supported by certificates or official records wherever possible.

Python C C++ SQL MATLAB

Scikit-learn TensorFlow Keras PyTorch Pandas NumPy Matplotlib Seaborn Plotly Jupyter Notebook Google Colab

OpenCV YOLO Image Processing Object Detection

LLMs RAG Embeddings ChromaDB Google ADK Gemini AI Agents Multi-Agent Workflows FastAPI

React Node.js Express MongoDB MySQL PostgreSQL REST APIs JWT

Git GitHub AWS EC2 AWS S3 Elastic Beanstalk BigQuery Vertex AI VS Code PyCharm

Qiskit Quantum Information Quantum Error Correction Cirq

PyTorch Hugging Face Transformers NLP Reinforcement Learning RAG Evaluation LangChain AI Agent Frameworks MLOps Data Engineering System Design GPU Computing

Skill depth varies. This list includes practical exposure, tools used in projects, and active learning areas; it should not be interpreted as expert proficiency in every item.

• IBM — Data Analysis with Python.
• IBM — Data Analytics Essentials.
• Cisco — Data Science Essentials with Python.
• Kaggle — Intro to Deep Learning.
• Google Cloud Gen AI Academy APAC Edition Cohort 2 — completion reported.

AI, Machine Learning & Data

• Kaggle Python.
• Kaggle Pandas.
• Kaggle Intro to Machine Learning.
• Kaggle Intermediate Machine Learning.
• Kaggle Feature Engineering.
• Kaggle Intro to SQL.
• Kaggle Advanced SQL.
• Deep-learning and model-evaluation coursework.

Generative AI & AI Fluency

• Anthropic Claude 101.
• Anthropic AI Fluency Foundations.
• Anthropic AI Fluency for Educators.
• Google Cloud learning involving Vertex AI, BigQuery ML, Responsible AI, and AI agents.
• Google for Startups Startup School: Agentic AI — completion status needs verification.
• Scaler RAG Pipelines.
• Scaler RAG vs AI Agents vs Agentic AI.
• Scaler Cracking AI Roles.

Cloud & Infrastructure

• AWS Educate ML Foundations.
• AWS Educate Intro to GenAI.
• AWS EC2, S3, and Elastic Beanstalk learning.
• Cisco Operating System Basics.

Quantum Computing

• Qiskit Global Summer School 2026 — participation reported.
• IBM Basics of Quantum Information.
• Google Quantum AI and quantum-error-correction learning resources.
• Cirq fundamentals.

Other Learning

• MATLAB ML Onramp.
• NVIDIA Jetson Nano learning.
• IBM Data Science Professional Certificate — completion needs verification.
• Microsoft AI Genius Certified badge — exact credential requirements need verification.
• Microsoft AI Skills Fest 2026 badge — details need verification.

Only credentials that can be substantiated should be presented as completed certifications. Registration, attendance, coursework, and completion certificates are different categories.

Statistics are provided by external services and may have availability, caching, or repository-coverage limitations.

• Secure a strong AI or machine-learning internship.
• Develop deeper skills in applied AI engineering and software development.
• Build research-oriented projects with reproducible results.
• Strengthen PyTorch, deep learning, NLP, and reinforcement learning.
• Improve DSA, system design, testing, and software engineering fundamentals.
• Explore AI agents, RAG evaluation, and reliable multi-agent systems.
• Continue lunar-image registration and engineering-simulation work where feasible.
• Prepare for GATE DA 2027 with a strong-rank target.
• Contribute more consistently to open-source AI and developer-tool projects.
• Document project architecture, tests, limitations, and measurable results.
• Build a credible portfolio of practical, technically rigorous projects.

I'm interested in practical AI, engineering, research, and open-source collaboration. I value clear problem definitions, reliable implementations, reproducible experiments, and honest evaluation.

Building at the intersection of AI, engineering, research, and real-world impact. 🤖