Professional Overview
Start with prompting for baseline tests. Use RAG when the model needs dynamic, proprietary, or updating data. Use fine-tuning only to teach format, style, or specific syntax on consistent data—never purely for knowledge injection. AI Engineer specializing in LLMs, prompt engineering, and intelligent automation. Experienced in building production-ready workflows and RAG pipelines using Python, Long chain, and vector databases. Passionate about evaluating model performance, reducing hallucinations, and integrating scalable AI solutions into real-world applications AI specialist focused on LLM alignment, data annotation, and AI system evaluation. Proficient in Python, prompt optimization, and quality assurance for generative AI models. Dedicated to maintaining high benchmark standards, accurate data labeling, and robust model outputs.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
data science & analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
AI Business, Marketing & Strategy
Apply AI to marketing automation, content generation, customer segmentation, and revenue growth strategies.
