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SegmentationI have hands-on experience supporting AI/ML and technical data workflows through DevOps, automation, documentation, and quality review. My background includes working with AI/ML engineers to support model deployment pipelines, monitor ML workloads, validate technical outputs, and improve reliability across cloud-based environments. I have also reviewed logs, deployment results, code changes, and system behavior to identify errors, inconsistencies, and quality issues, which aligns closely with data labeling, AI response evaluation, and model output review. In addition, I have strong experience following structured guidelines, writing clear technical documentation, reviewing content for accuracy, and using tools such as Python, Bash, Git, GitHub Actions, Jenkins, Docker, Kubernetes, AWS, and Azure. This gives me a strong foundation for AI training tasks such as prompt-response evaluation, data annotation, classification, hallucination detection, rubric-based scoring, technical content review, and coding-related model evaluation. My DevOps and cloud background allows me to handle both general data labeling work and more advanced technical AI evaluation tasks.
Yes. I have worked as an AI evaluator on Handshake, reviewing AI-generated responses against task guidelines and scoring rubrics. My work includes checking accuracy, instruction-following, relevance, clarity, hallucinations, and overall response quality, then providing structured feedback to improve model performance.
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I evaluate AI-generated responses
