Emmanuel Afriyie

Key Skills

Software

Google Cloud Vertex AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Fine Tuning

Freelancer Overview

I specialize in building and managing advanced AI and data pipelines, with hands-on experience in data labeling, annotation, and training data management across healthcare, regulatory, and research domains. My work includes designing and deploying retrieval-augmented generation (RAG) systems, developing multi-agent orchestration frameworks, and implementing robust data governance and audit trails to ensure compliance and data integrity. I have applied de-identification standards, handled sensitive PHI/PII data, and built custom scoring engines to assess data quality and model faithfulness. My technical expertise spans Python, PyTorch, TensorFlow, Hugging Face, Pinecone, and cloud platforms like AWS and GCP, as well as frontend and backend development. I am passionate about creating reliable, scalable AI solutions that prioritize data quality, privacy, and regulatory compliance.

Labeling Experience

Front-end/AI Engineer

Google Cloud Vertex AITextFine Tuning

Scope: Neonatal DAO is an AI-powered medical assistant specialized in pediatric and neonatal healthcare. It spans the full lifecycle from conception to early childhood, organized across 4 clinical domains: Neonatal — Newborn care (jaundice, NICU, feeding, screening, premature infant management) Postnatal — Maternal recovery, breastfeeding, postpartum mental health, infant development milestones Pregnancy — Prenatal care, gestational conditions, labor/delivery preparation, fetal development Fertility — Conception, IVF/IUI, PCOS, ovulation tracking, male/female factor infertility Data Labelling Tasks: Domain classification,Category tagging, Instruction type classification, Source attribution & Schema standardization Size: 94GB Quality Measures Adhere To: Automated Quality Scoring (multi-dimensional), Content Filtering & Deduplication, Safety Guardrails, Dataset-Level Quality Metrics & Synthetic Data Quality.

Scope: Neonatal DAO is an AI-powered medical assistant specialized in pediatric and neonatal healthcare. It spans the full lifecycle from conception to early childhood, organized across 4 clinical domains: Neonatal — Newborn care (jaundice, NICU, feeding, screening, premature infant management) Postnatal — Maternal recovery, breastfeeding, postpartum mental health, infant development milestones Pregnancy — Prenatal care, gestational conditions, labor/delivery preparation, fetal development Fertility — Conception, IVF/IUI, PCOS, ovulation tracking, male/female factor infertility Data Labelling Tasks: Domain classification,Category tagging, Instruction type classification, Source attribution & Schema standardization Size: 94GB Quality Measures Adhere To: Automated Quality Scoring (multi-dimensional), Content Filtering & Deduplication, Safety Guardrails, Dataset-Level Quality Metrics & Synthetic Data Quality.

2025

Education

Work History