Ariharan K C

Machine Learning Engineer Intern with 11 months experience

Talent

KarurMember since May 1, 2026

pythondjango developerFull Stack Pythonpython flask developerFastAPImachine learning engineerdeep learning engineerPyTorchtensorFlowReact.jsNext.jsNode.jsPostgreSQLLangChainLLMMongoDBHTMLCSSVIT transformersTransformersRAG

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pythondjango developerFull Stack Pythonpython flask developerFastAPI

About

AI/ML Engineer with hands-on experience developing, testing, and deploying machine learning models and AI-driven solutions. Proficient in Python, TensorFlow, PyTorch, and Scikit-learn. Experienced across supervised and unsupervised learning, deep learning (CNN, ViT), natural language processing (NLP), and generative AI using Large Language Models (LLMs). Skilled in data cleaning, preprocessing, and feature engineering to improve model accuracy.

Tamil

Nadu-based candidate with strong problem-solving, curiosity, and teamwork skills.

Experience

Machine Learning Engineer Intern at Ideas New Info Tech (2025-05 – 2026-03)

Model Development: Assisted in coding, building, and fine-tuning supervised and unsupervised machine learning models — regression, classification, and ensemble methods — using Python, Scikit-learn, XGBoost, and neural network architectures.
Data Preparation: Performed data cleaning, normalization, and feature engineering on structured business datasets to enhance model performance; optimized preprocessing pipelines, improving efficiency by 37% and reducing training time.
Experimentation: Ran tests, analyzed results, and documented model performance using precision, recall, F1-score, and cross-validation to continuously improve accuracy and efficiency.
End-to-End ML Pipeline: Designed and deployed complete ML pipelines — data ingestion → preprocessing → feature engineering → model training → evaluation → inference — using Python and FastAPI.
Predictive Modeling: Built forecasting and classification models using XGBoost, Prophet, and NeuralProphet; applied hyperparameter tuning and benchmarking across multiple architectures.
Anomaly Detection: Applied time-series decomposition (trend, seasonality, residuals) to detect irregular patterns and outliers.
Collaboration: Worked alongside senior ML engineers on AI initiatives; contributed to software development cycles in a team-focused, Agile environment, translating business requirements into ML solutions.
Documentation: Documented experiments, model performance metrics, and pipeline processes to ensure clarity and reproducibility.