Olaosebikan A.

Key Skills

Software

AWS SageMaker

Label Studio

Mindrift

OneForma

Prodigy

Scale AI

Surge AI

Other

Top Subject Matter

AI/ML Evaluation

Llms Domain Expertise

Scientific & Business Workflows

Top Data Types

Document

Image

Text

Top Task Types

Computer Programming/Coding

Evaluation/Rating

Object Detection

Prompt + Response Writing (SFT)

RLHF

Freelancer Overview

Lead. AI/ML Engineer – LLM Systems & Evaluation. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Generative AI Output Evaluation (RLHF & Reasoning)

AWS SageMakerTextRLHFFine-tuning

I evaluated complex Generative AI responses for factual accuracy, reasoning logic, and safety compliance. The work involved "Human-in-the-Loop" (HITL) review where I identified subtle hallucinations, graded the helpfulness of responses, and wrote original "Golden" responses to train the model. This role required deep research to verify facts and a strong understanding of prompt engineering to test model limitations.

2024 - 2023

Technical Project Lead – AI Systems

Text

As Technical Project Lead for an AI systems team, I verified outputs of AI-driven processing tasks for compliance with logic and mathematical constraints. I designed and ran validation workflows using structured Python methods. I ensured all output data matched strict evaluation standards. • Administered computationally intensive data handling tasks • Conducted output quality reviews with programmatic checks • Led architectural oversight of AI evaluation process • Maintained rigorous standards for accuracy in result verification.

2022 - 2023

Machine Learning Engineer

Text

As Machine Learning Engineer, I designed evaluation workflows to assess model outputs in real-time predictive settings. I conducted statistical tests and hypothesis validation to ensure quality of algorithmic decisions. This included evaluating models on shifting data distributions for robust production performance. • Built computational frameworks for continuous evaluation • Applied advanced analytics for output validation • Ensured high reliability in dynamic environments • Used Python, TensorFlow, and data science libraries for outcome verification.

2021 - 2023

Education

Work History