Vikram S.

Key Skills

Software

No software listed

Top Subject Matter

Ai/ml Domain Expertise

Deep Learning

Object Detection

Top Data Types

Text

Computer Code Programming

Top Task Types

Object Detection

Classification

Freelancer Overview

AI/ML Intern at Edunet foundation (AI model training and deployment). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Microsoft SQL Server. Education includes Advance Diploma in Information Technology, NSTI Bengaluru (2014) and HSC, Govt. S. S. School (2015). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Object Detection and Classification.

Labeling Experience

AI/ML Intern at Edunet foundation (AI model training and deployment)

Object Detection

Worked on developing, training, and deploying machine learning, deep learning, and object detection models for real-world problem solving. Focused on building AI solutions that translate data into actionable outcomes while supporting innovation within the project scope. This role emphasized model development and evaluation rather than manual annotation tooling.• Developed and trained ML/DL models as part of end-to-end AI workflows.• Built object detection components for applied use cases.• Supported deployment of trained models for practical inference needs.• Collaborated on solving complex problems using advanced AI techniques.

2024 - 2024

Data Analyst at Blinque Technology (dataset validation and QA)

TextClassification

Performed data analysis work that included defining SQL QA queries and evaluating datasets using statistical and visualization techniques. While not explicitly a labeling/annotation role, the work involved validating and assessing data quality to support downstream business and production use. The responsibilities centered on preparing and checking structured data for reliable use in analytics and reporting.• Designed SQL quality assurance queries across source-to-target tables using transformation rules.• Compared business vs. production data with efficient lookup/QA validation (~99% efficiency).• Evaluated datasets using outlier analysis, boxplots, V-lookup, and chi-square tests.• Supported data collection, processing, and analysis to meet business requirements.

2021 - 2022

Education

Work History