Picasso M.

Key Skills

Software

Don't disclose

Top Subject Matter

Clinical Medicine (AI evaluation & prompt operations)

Clinical Medicine evaluation and consultant review

Legal Services & Contract Review

Top Data Types

Text

Image

Document

Top Task Types

Prompt + Response Writing (SFT)

Text Generation

Transcription

Computer Programming/Coding

Data Collection

Function Calling

RLHF

Red Teaming

Question Answering

Text Summarization

Fine-tuning

Classification

Freelancer Overview

Independent AI Application Developer & Prompt Specialist (AI-assisted app development, prompt optimization, QA). Brings 2+ years of experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling Education includes Diploma in Clinical Medicine and Surgery, Kenya Medical Training College (KMTC) (2023). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Prompt + Response Writing (SFT), Evaluation, and Rating.

Labeling Experience

Independent AI Application Developer & Prompt Specialist (AI-assisted app development, prompt optimization, QA)

TextPrompt + Response Writing (SFT)

Developed and tested prompt matrices to drive generative outputs and improved prompt quality through iterative feedback. Calibrated multimodal rendering-related prompt parameters to achieve consistent, structurally valid results. Performed functional QA to identify edge cases and ensure downstream model behavior aligned with expected outputs. • Structured descriptive prompt matrices for advanced generative frameworks • Calibrated rendering parameters (e.g., lighting, textures, camera composition) • Validated user-experience flows and detected logical/structural anomalies • Verified conditional workflow behavior in node-based automation

2025 - Present

Clinical Medicine Evaluator & Consultant (AI/LLM evaluation & verification in clinical domain)

Don't disclose

Evaluated clinical reasoning outputs using evidence-based rubrics to ensure technical accuracy and safety alignment. Verified complex clinical data synthesis, including lab profiles and treatment histories, for consistency with safety thresholds. Applied strict guideline adherence and confidentiality/compliance constraints during high-stakes evaluation workflows. • Assessed medical logic, disease classification, and case formulations • Checked alignment with national medical protocols and diagnostic rubrics • Verified data consistency across literatures, labs, and treatments • Managed confidentiality and compliance for sensitive medical data

2024 - Present

Education

Work History