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Adithya S.T.Software Engineer
Email
adithya.2018@vitalum.ac.in
Phone
9840983499
https://adithyaportfolio.lovable.app
Location
Chennai
LinkedIn
www.linkedin.com/in/adithya-st
Medium
https://medium.com/@adithya1010
GitHub
https://github.com/adithya1010
Profile

Energetic and dedicated Software Engineer with a Master's degree in Software Engineering and specialized training in AI with Machine Learning. I am eager to apply my knowledge of project coordination and completion to achieve company goals.

Education
AI with Machine Learning Short Term Skill Program, Naan Mudhalvan Finishing School⁠
  • • Learnt about Supervised Learning, Unsupervised Learning, Reinforcement Learning in this course
  • •Have completed projects related to each topic and have also given presentations on topics related to AI
    • Dec 2024 – Mar 2025Chennai, India
    M.Tech Software Engineering, Vellore Institute of Technology⁠
  • •CGPA-7.09
    • 2018 – 2023Chennai, India
  • •Passed with 89.17% (1070/1200)
    • 2016 – 2018Chennai, India
    Secondary Education, Chinmaya Vidyalaya⁠
  • •Passed with 8.1 CGPA
    • 2011 – 2016Chennai, India
    Work Experience
    Post Graduate Engineer Trainee, HCLSoftware⁠

    Band: E1.2

    Department: HCLSW DRYiCE-DRYiCE AIOPS-PROD-IAUTO

    Feb 2024 – May 2024Chennai, India

    Unit: Delivery Unit (DU)

    Machine Learning Intern, Orinson Technologies⁠

    Developed models for object detection, predicting numerical values and performance of students using datasets from Kaggle and tools like Tensorflow, Numpy, Pandas

    Aug 2024 – Sep 2024Chennai, India
    AI Software Engineer, Abhinaya Limited⁠
  • •Worked on DocAssist – an AI-based support application for managing patient and doctor interactions
  • •Performed QA testing and debugging, improving app stability and performance
    • Jul 2025Chennai, India
    Adithya S.T.
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  • •Used Git for version control and collaborated with the team via GitHub and Jira
    • Research Trainee, NIT Delhi⁠
  • •Engineered an end-to-end differentiable portfolio optimization framework using a Multi-Modal Transformer, successfully solving the "Predict-Then-Optimize" decoupling problem.
  • •Achieved superior risk-adjusted performance in temporal walk-forward testing, delivering a 1.3245 return factor while limiting maximum drawdown to -5.12% during simulated market crashes.
    • Jun 2026 – Jul 2026Delhi, India
    Skills
    Python
    Java
    PowerPoint
    HTML/CSS
    C++
    Word
    Notion
    JavaScript
    Languages
    English
    Tamil
    Hindi
    French
    Publications

    Simple project to demonstrate Web Scraping using Python

    A Guide to install WSL 2 and run GUI apps on Windows devices

    Certificates
    Adithya S.T.
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    Projects
  • •Implemented an end-to-end convolutional neural network pipeline in a Jupyter Notebook to detect brain tumors from MRI images, including data ingestion, image preprocessing (resizing, normalization) and model training.
  • •Improved model robustness through data augmentation, class-balancing strategies and regularization (early stopping/dropout), and iteratively tuned hyperparameters and architectures to boost generalization.
    • Feb 2023 – Mar 2023
  • •Validated and documented results with standard evaluation metrics (accuracy, precision, recall, F1, ROC‑AUC) and clear experiment notes to enable reproducibility and handoff.
  • •Developed "Orinson-Task-4" as an interactive, end-to-end Jupyter Notebook during the Orinson Technologies internship, implementing data ingestion, preprocessing, exploratory analysis, and visualization to extract actionable insights.
  • •Wrote modular, well-documented Python code and reusable notebook cells to ensure reproducibility and simplify handoff; included clear narrative, charts, and inline explanations to guide reviewers through methodology and results.
    • Aug 2024 – Sep 2024
  • •Managed the project in GitHub (repo: Orinson-Technologies-Internship), applying version control best practices and delivering a polished, notebook-based deliverable demonstrating technical proficiency with data analysis and prototyping.
  • •Built a customer-satisfaction prediction pipeline in Python using a public Kaggle dataset (Invistico_Airline.csv): performed EDA, cleaned and encoded categorical features (Age, Customer Type, Class, service ratings, delay minutes), and created train/test splits with pandas and scikit-learn.
  • •Trained and tuned a Random Forest classifier (GridSearchCV, PredefinedSplit) to optimize model performance and evaluated it with accuracy, precision, recall and F1 metrics to ensure balanced performance on satisfaction classes.
    • Feb 2025 – Feb 2025
  • •Serialized the final model with pickle and extracted feature importances to surface actionable insights (service quality and delay-related factors as primary drivers of satisfaction) for stakeholders.
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