I provide high-quality data labeling and annotation services for AI and machine learning teams that need accurate, consistent, and well-documented training data. With experience across multiple annotation platforms including Label Studio, and a background working with companies like Turing, Micro1, and TrainAI/RWS, I bring both technical understanding and labeling rigor to every project.

Computer Vision Annotation Bounding box annotation, object detection labeling, image classification, and segmentation tasks for CV pipelines. I'm comfortable working with complex and edge-case scenarios that require careful judgment rather than just pattern-following.

Multimodal & VLM Evaluation Rating and evaluating model outputs derived from combined image and text inputs across criteria such as instruction following, content retention, visual quality, AI-generated content detection, and response coherence. Useful for teams fine-tuning or evaluating Vision Language Models and Multimodal LLMs.

RLHF & Model Feedback Human feedback collection and response ranking for reinforcement learning pipelines. I've worked on RLHF tasks that require nuanced preference judgments, making me well-suited for alignment and fine-tuning projects.

RAG Pipeline Annotation Labeling and evaluating retrieval-augmented generation outputs, including relevance scoring, context grounding, and answer quality assessment — helping teams ensure their RAG systems return accurate and useful responses.

NLP & Text Annotation Named entity recognition, intent classification, sentiment labeling, and text categorization for NLP models. I've also built independent NLP projects including a context-aware feedback classification system, giving me a developer-level understanding of how annotations translate into model behavior.

Quality Assurance & Process Improvement Beyond labeling, I've reviewed and QA'd other annotators' work, flagged inconsistencies, and contributed to refining labeling guidelines. I can serve as a senior annotator or QA reviewer on larger teams.

I understand what the data is for. Whether you're training a detection model, fine-tuning a multimodal LLM, or evaluating a RAG pipeline, I bring context to the work that improves output quality and reduces rework. I'm detail-oriented, follow complex rubrics accurately, and communicate clearly when guidelines need clarification.

Ideal for startups, AI labs, and research teams that need a reliable, experienced annotator who can hit the ground running.