Aligarh, Uttar Pradesh, India
I am a detail-oriented AI Data Annotation & LLM Evaluation Specialist with 1.8 years of professional experience delivering high-quality, precise, and deadline-driven results across image, video, 3D/point cloud, geospatial datasets, and AI model evaluation. I specialize in image/video annotation, bounding boxes, polygon annotation, semantic segmentation, 3D point cloud labeling, LiDAR data processing, GIS mapping, and LLM response & prompt evaluation, consistently maintaining accuracy levels above 99%. My experience includes scene labeling, segment labeling, hazard detection, object detection & classification, content moderation, and QA checks for AI/ML training datasets in sectors such as autonomous driving, remote sensing, agriculture tech, surveillance, and LLM evaluation projects. I have contributed to projects for top clients like NVIDIA (via Randstad) and Humanloop-assisted LLM evaluation tasks, ensuring data consistency, high accuracy, and optimized workflows across large-scale datasets. š” Core Expertise & Skills: Annotation & Labeling: Bounding Boxes, Polygon Annotation, Semantic Segmentation, 3D Point Cloud Labeling, LiDAR & Satellite Data, Video Annotation LLM & AI Evaluation: Prompt Testing, Response Ranking, Hallucination Detection, Reasoning Error Identification, Guideline Compliance Data Management & QA: Data Entry, Cleaning, Validation, Consistency Checks, Error Detection, Feedback Implementation š Technical Skills & Tools: Humanloop, Labelbox, CVAT, Supervisely, ArcGIS, QGIS, LiDAR360, SQL, MS Excel, Google Sheets, Python (Basics), Java (DSA) I thrive in fast-paced, high-quality, AI-driven environments, leveraging analytical thinking, attention to detail, and workflow optimization to improve dataset quality, efficiency, and AI model performance. My combined experience in data annotation production and quality control provides a 360-degree understanding of the data lifecycle ā from raw input to AI-ready datasets, making me well-equipped for both computer vision annotation and LLM evaluation roles.
ā¢ Evaluated AI-generated prompts and responses in English and Urdu, assessing correctness, truthfulness, grammar, tone, clarity, and guideline compliance. ā¢ Compared and ranked multiple model outputs; selected the best response and provided concise, well-reasoned justifications for each decision. ā¢ Filled detailed evaluation tags (accuracy, safety, relevance, coherence, etc.) to support high-quality LLM training datasets. ā¢ Tested prompts by adding constraints and verified whether the model responses met all required conditions. ā¢ Labeled reasoning errors, hallucinations, missing steps, tone issues, and policy-violating content across diverse task types. ā¢ Performed structured data labeling and classification tasks, contributing to overall linguistic quality improvement. ā¢ Navigated internal annotation tools, resolved minor UI issues, and maintained proper documentation and file naming. ā¢ Reviewed sensitive content with full confidentiality and delivered accurate, guideline-aligned evaluations. ā¢ Collaborated remotely to clarify instructions, refine evaluation rules, and improve task quality.
Delivered high-precision image, video, and 3D LiDAR point cloud annotations for AI & Machine Learning models, maintaining 99%+ accuracy across multiple large-scale autonomous driving projects. Annotated datasets using Bounding Boxes, Polygon Annotation, Semantic Segmentation, Keypoints/Landmarks, and 3D Cuboid Labeling to train advanced computer vision systems. Performed LiDAR point cloud labeling for vehicle, pedestrian, and hazard detection, integrating 3D spatial data with camera imagery for sensor fusion applications. Executed GIS-based mapping and geospatial labeling of satellite/aerial imagery, supporting urban mapping and navigation systems. Directed scene labeling, segment labeling, and hazard identification workflows for driverless car datasets, ensuring data integrity and compliance with project guidelines. Used advanced annotation platforms like Humanloop, CVAT, Labelbox, Supervisely, and tools like ArcGIS, QGIS, LiDAR360 for spatial and object-level labeling. Collaborated with cross-functional teams to optimize labeling guidelines, improve annotation speed by 15%, and reduce error rates through rigorous QA reviews. Provided training and mentorship to new team members, standardizing workflows and ensuring consistent output quality.
Worked as a dedicated social media manager for the esteemed NGO, Pahel Foundation. Spearheaded initiatives to bolster the organization's online presence, focusing on strategic management of Instagram. Achieved significant growth in Pahel Foundation's Instagram following through targeted content, engagement campaigns, and community outreach efforts. Contributed to amplifying the organization's impact and promoting its noble causes through effective social media strategies.