Hire this AI Trainer
Sign in or create an account to invite AI Trainers to your job.
Appen
OneForma
Text
Audio
Document
Image
RLHF
Data Collection
Classification
Fine-tuningAI Trainer & Data Annotator — CrowdGen by Appen (ADAP Platform). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Appen, RWS, and OneForma. AI-training focus includes data types such as Text, Audio, and Document and labeling workflows including RLHF, Data Collection, and Classification.
TextManaged project deliverables and quality metrics within the Moravia Portal workflow. Coordinated submission timing and quality tracking to ensure annotated datasets met project requirements. • Managed project queues and deliverable timelines • Tracked quality metrics for annotation work • Liaised with project managers through the portal • Ensured accurate submissions per task requirements
TextData CollectionOperated under GDPR and privacy-first workflows when handling sensitive medical content, including PII and CBI, under NDA. Followed secure data handling protocols while working on annotation and validation tasks. • Handled PII/CBI with required data security protocols • Followed GDPR-compliant workflow requirements • Worked under signed NDA restrictions • Maintained privacy-first handling during labeling work
TextFine-tuningCurated domain-specific LLM fine-tuning data by preparing and organizing high-quality annotations for generative AI training workflows. Supported RLHF and fine-tuning phases through cleaned, task-aligned training inputs. • Prepared annotations for generative AI fine-tuning • Supported RLHF data workflows • Ensured domain relevance of curated labels • Prepared training-ready inputs for downstream use
TextCarried out Human-in-the-Loop validation by reviewing, correcting, and quality-checking AI-assisted annotations. Ensured accuracy, fairness, and inclusivity before labels were accepted for training. • Reviewed AI-assisted annotation outputs • Corrected and quality-checked label correctness • Enforced fairness and inclusivity quality criteria • Delivered validated annotations for model training data
TextPerformed Named Entity Recognition annotation by identifying and categorizing entities in unstructured text. Included medical and biomedical entity types to support NLP pipelines. • Tagged persons, organizations, locations, and dates • Categorized other relevant entity types • Applied NER standards to unstructured clinical text • Supported medical/biomedical NLP model training
Pharmacy, Pharmaceutical sciences
Community Pharmacist
