Vineeti Manekar

AI/ML • 21 years • Mumbai, India • UTC+05:30 • Work permits: India • English: C2 • Can start now

Artificial Intelligence Developer

C++

SQL

Java

Python

Active 1 day ago

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Overview

Technical skills

Projects

Timeline

Roles

Overview

Data science postgraduate candidate with project-based experience in Python-based machine learning and deep learning, including computer vision and anomaly detection. Built ML/DL solutions using TensorFlow and Pandas and applied model evaluation and feature engineering for predictive-maintenance and time-series style problems. Developed small AI/web applications using Flask and Gradio and explored explainability concepts for interpretable outputs.

Technical skills

Languages

C++

SQL

Java

Python

DevOps

Azure

GCP

GitHub

Docker

Git

AI/ML

LLM

NumPy

OpenCV

Pandas

PyTorch

Scikit-learn

TensorFlow

Gradio

SHAP

Hadoop

Hugging Face

Spark

Machine Learning

Prompt Engineering

Other

Flask

TensorFlow C++

Power BI

Databricks

Tableau

Multimodal AI

NLP

Computer Vision

Transfer Learning

Explainable AI

Time Series

Anomaly Detection

Projects

Live Attendance Tracker

Implemented a Python computer-vision workflow for automated attendance capture. Used the CV pipeline to detect and process inputs for attendance purposes. Reported an improvement outcome by reducing manual effort compared to manual attendance recording.

Calorie Management App

Built a Flask application to help track daily food intake and exercise data. Implemented logic to record consumed items and estimate calories burned from exercise. Provided an end-to-end app workflow using Python backend services.

NASA Turbofan Engine Remaining Useful Life (RUL) Prediction

Developed a predictive-maintenance workflow for the FD001 dataset. Performed data preparation, computed remaining useful life targets, and carried out exploratory analysis and feature engineering. Trained regression models for RUL prediction and added a binary classification objective to flag engines approaching failure. Included feature-importance analysis to support model interpretation.

Anomaly Detection in Power-Grid Energy Consumption

Created an anomaly detection workflow for hourly power consumption data. Engineered time-based features and used PCA to explore underlying patterns. Applied K-Means clustering and Isolation Forest to identify unusual observations and visualized anomalies over time for monitoring.

Integrated Health Intelligence Platform (IHIP)

Built an explainable AI pipeline for early disease detection using brain MRI image classification. Implemented transfer learning with TensorFlow and EfficientNetB0 for multi-class tumor identification. Designed a multimodal decision-support concept linking MRI findings with blood-biomarker risk and biomedical literature retrieval. Proposed explainability via Grad-CAM and SHAP and included a plan for plain-language summarization in a Gradio interface.

Timeline

Artificial Intelligence Engineer Intern • Junior

Sapio Analytics • Internship

In office

Developed functionality for the Saksham Mobile Career Path application and consolidated partially implemented code into a unified codebase. Managed the project using GitHub workflows and performed deployment and testing tasks, including API key configuration and API response validation. Prepared AI resources content from job and course descriptions and created marketing documentation to explain application features and user value.

GitHub