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Shalini S K

Shalini S K

AI Engineer / Product & SDK Lead – GovernHQ

India flagBengaluru, India
$30.00/hrExpert

Key Skills

Software

No software listed

Top Subject Matter

LLM and scheduling AI systems
AI-powered scheduling and automation
Structured business and operational datasets

Top Data Types

TextText
ImageImage

Top Task Types

SegmentationSegmentation

Freelancer Overview

AI Engineer / Product & SDK Lead – GovernHQ. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Technology, Garden City University (2022). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Segmentation.

ExpertEnglishHindiGermanKannada

Labeling Experience

AI Engineer – Mira (Stealth AI Startup)

Text
Built and implemented validation pipelines to assess decision-making processes in AI-powered scheduling systems. Developed workflows to analyze and rate over 1000 user inputs for system optimization. Focused on iterative feedback cycles to refine and improve the AI system's reliability and efficiency. • Labeled and validated data for AI-driven scheduling • Rated and reviewed outputs to guide model improvement • Ensured quality and accuracy through structured data evaluation • Used proprietary tools for system validation tasks

Built and implemented validation pipelines to assess decision-making processes in AI-powered scheduling systems. Developed workflows to analyze and rate over 1000 user inputs for system optimization. Focused on iterative feedback cycles to refine and improve the AI system's reliability and efficiency. • Labeled and validated data for AI-driven scheduling • Rated and reviewed outputs to guide model improvement • Ensured quality and accuracy through structured data evaluation • Used proprietary tools for system validation tasks

2026 - Present

AI Engineer / Product & SDK Lead – GovernHQ

Text
Designed AI evaluation workflows for LLM and automated scheduling systems. Led analysis of model outputs to identify gaps and improve AI system reliability by rating responses. Contributed to enhancing decision accuracy and developing evaluation metrics for AI system analysis. • Evaluated and rated AI outputs across 10+ different use cases • Collaborated with teams to align evaluation methodology with product goals • Improved model reliability by identifying issues in AI system outputs • Utilized internal or proprietary tools to structure and execute evaluation pipelines

Designed AI evaluation workflows for LLM and automated scheduling systems. Led analysis of model outputs to identify gaps and improve AI system reliability by rating responses. Contributed to enhancing decision accuracy and developing evaluation metrics for AI system analysis. • Evaluated and rated AI outputs across 10+ different use cases • Collaborated with teams to align evaluation methodology with product goals • Improved model reliability by identifying issues in AI system outputs • Utilized internal or proprietary tools to structure and execute evaluation pipelines

2026 - 2026

AI Intern – Infosys

Text
Conducted exploratory data analysis and labeled data attributes in structured datasets for AI training. Supported evaluation and labeling activities to improve AI model accuracy and performance. Communicated insights and documented results to optimize model evaluation processes. • Labeled and validated structured data for machine learning models • Performed evaluation and feature engineering for data annotation • Improved data quality through systematic labeling and EDA • Utilized Python and internal tools for annotation and analysis

Conducted exploratory data analysis and labeled data attributes in structured datasets for AI training. Supported evaluation and labeling activities to improve AI model accuracy and performance. Communicated insights and documented results to optimize model evaluation processes. • Labeled and validated structured data for machine learning models • Performed evaluation and feature engineering for data annotation • Improved data quality through systematic labeling and EDA • Utilized Python and internal tools for annotation and analysis

2025 - 2026

AI/ML Intern – Nija World Pvt. Ltd.

Text
Performed data preprocessing, cleaning, and labeling of structured datasets for ML model development. Optimized and evaluated model performance through detailed annotation and systematic data review. Contributed to system improvements by labeling data and assessing output quality. • Cleaned, labeled, and curated data for model training • Evaluated data attributes to optimize model accuracy • Documented data annotation workflows and process efficiencies • Used internal tools and Python for annotation tasks

Performed data preprocessing, cleaning, and labeling of structured datasets for ML model development. Optimized and evaluated model performance through detailed annotation and systematic data review. Contributed to system improvements by labeling data and assessing output quality. • Cleaned, labeled, and curated data for model training • Evaluated data attributes to optimize model accuracy • Documented data annotation workflows and process efficiencies • Used internal tools and Python for annotation tasks

2024 - 2024

LeproScan – Medical Image Segmentation (Project)

ImageSegmentation
Trained and evaluated U-Net models for medical image segmentation using labeled data. Annotated and segmented over 2,000 medical images for improved detection and model training. Conducted detailed performance analysis and ensured annotated dataset quality. • Labeled medical images for segmentation tasks • Generated and validated segmentation masks for training data • Used domain-specific knowledge for medical annotation processes • Utilized internal or proprietary tools for image labeling

Trained and evaluated U-Net models for medical image segmentation using labeled data. Annotated and segmented over 2,000 medical images for improved detection and model training. Conducted detailed performance analysis and ensured annotated dataset quality. • Labeled medical images for segmentation tasks • Generated and validated segmentation masks for training data • Used domain-specific knowledge for medical annotation processes • Utilized internal or proprietary tools for image labeling

2023 - 2023

Education

G

Garden City University

Bachelor of Technology, Artificial Intelligence and Machine Learning

Bachelor of Technology
2022

Work History

P

PM Accelerator

Frontend Engineer & AI Engineer Intern

Bengaluru
2026 - Present
M

Mira

AI Engineer

Bengaluru
2026 - Present