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DICKSON MWITADM
Open to opportunities

DICKSON MWITA

@dicksonmwita

AI Training Specialist evaluating LLM outputs for safer, accurate performance.

Tanzania
Message

What I'm looking for

I’m looking for remote, project-based work where I can evaluate LLM responses using detailed rubrics, apply taxonomies, and deliver consistent written feedback that improves accuracy, safety, and performance—while staying reliable and self-managed.

I’m an AI Training Specialist and detail-oriented generalist focused on evaluating and annotating large language model outputs. I use structured rubrics and predefined taxonomies to categorize, label, and improve dataset quality while strengthening reasoning quality, factual accuracy, and clarity.

Since 2024, I’ve worked remotely as a Freelance Data Annotator, applying labeling schemas and annotation guidelines to convert raw inputs into consistent, machine-usable outputs. From 2025 onward, as an AI Chatbot Evaluator with Outlier AI (Scale AI), I evaluate and rank LLM responses, flag hallucinations and guideline violations, and deliver actionable written feedback that supports AI safety and performance improvements.

I’m a reliable, self-directed remote contributor who thrives on guideline adherence, attention to detail, and clear communication. I also craft complex prompts that simulate realistic user needs and participate in onboarding and training assessments to maintain quality and eligibility across concurrent projects.

Experience

Work history, roles, and key accomplishments

IN
Current

Freelance Data Annotator

Independent

Jan 2024 - Present (2 years 5 months)

Annotated large datasets using labeling schemas and taxonomies to produce consistent, machine-usable outputs. Synthesized high volumes of information into structured labels while adhering to guidelines and quality standards for remote clients.

Education

Degrees, certifications, and relevant coursework

HC

High School Certificate

High School Certificate

Completed a High School Certificate in Dar es Salaam, Tanzania. Continued self-directed learning in AI evaluation methodologies, prompt engineering, and data quality best practices through Outlier training resources and project onboarding.

Tech stack

Software and tools used professionally

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