Postgraduate in Statistics with hands-on experience in building and deploying machine learning models and scalable data solutions to drive business impact. Skilled in predictive modelling, experimentation, and automated …
Postgraduate in Statistics with hands-on experience in building and deploying machine learning models and scalable data solutions to drive business impact. Skilled in predictive modelling, experimentation, and automated monitoring with a strong understanding of AI governance, model transparency, and responsible AI practices. Experienced in working with cross-functional teams to integrate data science into decision-making, optimize model performance, and support scalable infrastructure on cloud environments. Strong analytical mindset focused on delivering data-driven insights and operational efficiency.
Developed and integrated three interconnected AI features — a multilingual audio interview coach, a video-based confidence analyzer, and a context-aware question generator — into a unified edtech LMS platform under the guidance of senior engineers.
Managed containerized deployment of these components using Docker and CI/CD pipelines across development and staging environments.
Built and deployed multimodal AI systems (LLMs, speech, and video models) using embedding-level evaluation, automated drift monitoring, and model validation pipelines.
Reduced inference latency by ∼30%.
Improved system reliability across real-time AI workflows within cross-functional Agile sprints.
Architected, deployed, and optimized production-grade multimodal AI systems (LLMs, speech, and video models) using Dockerized microservices, embedding-level evaluation, automated drift monitoring, and CI/CD-driven model validation, reducing inference latency by ∼30% and improving system reliability across real-time AI workflows, delivered through Agile sprint-based development., Designed and delivered full-stack GenAI applications leveraging RAG pipelines, vector databases (FAISS/Pinecone), prompt orchestration, and REST APIs to automate intelligent content and decision workflows, accelerating feature delivery by ∼40% and increasing user engagement by ∼45%, collaboratively built and iterated within cross-functional Agile teams.
Independently drove market analytics at an early-stage agricultural drone startup.
Conducted price research, competitor analysis, and demand trend analysis using Python and SQL.
Delivered operational insights across drone coverage, flight efficiency, and sales indicators via Power BI dashboards.
Reduced manual reporting effort by ∼20%.
Improved targeting precision by ∼25%.
Built a machine learning price and demand prediction system for agricultural drones using Random Forest and XGBoost regressors.
Engineered seasonality, region, and competitor pricing features from internal sales and external market data.
Forecasted optimal pricing and monthly regional demand.
Evaluated models via RMSE and MAE to support inventory and sales planning.
Prepared, engineered, and validated structured datasets from agricultural and market data sources to support segmentation, demand modeling, and downstream machine learning workflows, improving targeting precision by ∼25% and enabling model-ready data pipelines for analytical and predictive use cases., Developed automated analytics and monitoring pipelines using Python, SQL, and Power BI to track operational performance (drone efficiency, coverage metrics, sales indicators), reducing manual reporting effort by ∼20% and accelerating data-driven operational and product decisions.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
