Results-driven AI Model Alignment and Evaluation Specialist backed by a Bachelor’s degree with Second Class Upper Division honors with extensive experience training, validating, and auditing frontier Large Language Models and multi-step AI agents. Armed with a strong background across global enterprise platforms, I specialize in Reinforcement Learning from Human Feedback (RLHF), adversarial red-teaming, rigorous rubric grading, and PII compliance. Whether you need precise data annotation, multi-step agent trajectory auditing, technical writing, or localized linguistic accuracy (English, Swahili, and Sheng), I deliver zero-subjectivity, high-precision results under strict quality thresholds. Let’s build smarter, safer digital systems.
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Applied the HHH (Helpful, Honest, Harmless) framework to rank, grade, and rewrite model responses, improving logical reasoning for complex enterprise workloads and maintaining a 96.4%+ accuracy rating.
Maintained rigorous data auditing standards over an intensive 30-hour weekly sprint capacity to
support model alignment cycles and frontier consumer-facing AI systems.
Identified logical inconsistencies, refined dataset inputs, and verified ground-truth alignment,
maintaining an error tolerance threshold below 1.5%.
Applied advanced reasoning frameworks to grade and audit model outputs against multi
layered enterprise rubrics, achieving a 98.7% accuracy baseline across high-priority evaluation
batches.
Scaled data production under rapid-turnaround parameters, managing high-priority batch
deliveries of multi-step agent logs per week under strict 24-hour turnaround parameters while
protecting data integrity.
Evaluated and annotated 5,000+ complex multi-domain datasets and benchmark prompts to train and validate frontier LLMs.
Maintained a 98.9% accuracy rate while enforcing strict BLUF formatting and signal-to-meaning reading comprehension standards.
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Comprehensive professional training focused on the architecture, operational mechanics, and multi-step execution workflows of Autonomous AI Agents.
Covered core competencies including agentic reasoning loops, task decomposition, tool integration, and quality assurance frameworks designed to validate complex, multi-turn agent trajectories in production environments.
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Focused independent study and continuous practical research into Large Language Model (LLM) architectures, constitutional AI safety alignment, adversarial vulnerability testing, and reinforcement learning mechanics.
Dedicated to staying at the forefront of emerging evaluation methodologies, token-level optimization, and rigorous instruction-following frameworks.
Focused on mastering the operational mechanics of small and medium enterprises (SMEs), business scaling, administrative frameworks, and modern entrepreneurship. This foundational training in structured business operations directly complements my precision-driven approach to data workflows and enterprise AI project management.
We will review the reports from both freelancer and employer to give the best decision. It will take 3-5 business days for reviewing after receiving two reports.