Applied AI Engineering
Across Every Domain
Building and deploying production-grade machine learning and deep learning systems, from research to real-world scale.
Tools & Technologies
From geospatial science and generative AI to cloud-native engineering, built and deployed end-to-end.
Machine Learning
Deep learning, time-series forecasting & multi-agent systems.
Generative AI & LLMs
Production LLM applications across all major providers.
Cloud & DevOps
AWS and GCP production infrastructure with containerised CI/CD.
GIS & Remote Sensing
Satellite imagery analysis, spatial modelling & climate data.
Data Science
End-to-end analysis pipelines from raw data to insight.
Visualization & Analytics
Interactive dashboards and publication-quality figures.
Work History
Seven years across academia, startups, and global consulting, from satellite-based water management research to enterprise AI platforms.
- Implemented a CNN-LSTM framework for actual evapotranspiration (ET) prediction using Landsat time-series and climatic parameters, enabling high-resolution irrigation demand assessment across Punjab.
- Providing end-to-end machine learning and deep learning consulting services, helping clients design, build, and deploy intelligent data-driven solutions tailored to their business objectives.
- Architected and deployed Dynaris' AI agent platform with team, designing supervisor, subagent workflows, document, web, runtime, and voice automation using LangGraph and FastAPI.
- Developed stacked ML models (MLP, SVR, Gradient Boosting) to forecast stock prices over multiple horizons (5-day, W1, W2), achieving low RMSE (1.5–4.6) and reliable predictive accuracy.
- Designed AI solutions for healthcare diagnostics (breast cancer, autism, dental imaging, rare diseases), improving accuracy and enabling earlier detection for multiple clients.
Publications & Conferences
Peer-reviewed journals, international conferences, and book chapters spanning dental AI, climate science, and water resource engineering.
AbuSalim, S., Zakaria, N., Mostafa, S. A., Maqsood, A., Abdulkadir, S. J., & Mokhtar, N.
Hierarchical deep learning model for tooth classification in intra-oral dental photographs.
Neural Computing and Applications, 37(32), 26659–26688 · 2025
DOIAbusalim, S., Zakaria, N., Maqsood, A., Saboor, A., Yew, K. H., Mokhtar, N., & Abdulkadir, S. J.
Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification.
IAES International Journal of Artificial Intelligence (IJ-AI), 13(2), 2081–2092 · 2024
DOINoor, R., Maqsood, A., Baig, A., Pande, C. B., Zahra, S. M., Saad, A., Anwar, M., & Singh, S. K.
A Comprehensive Review of Water Pollution in the South Asia Region: Pakistan.
Urban Climate, 48, 101413 · 2023
DOINoor, R., Arshad, A., Shafeeque, M., Liu, J., Baig, A., Ali, S., Maqsood, A., et al.
Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin.
Remote Sensing, 15(2), 318 · 2023
DOIAbuSalim, S., Zakaria, N., Maqsood, A., Ismail, Z., Jiann Yeou, & Saboor, A.
Predicting the Flow of Aqueous Humor in the Eye with Descemet Membrane Detachment using Machine Learning Algorithms.
Neural Computing and Applications · 2025
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Get in Touch
Open to research collaborations, consulting engagements, and full-time opportunities.
contact@aarishmaqsood.com
in/aarishmaqsood
Location
Lahore, Pakistan
Open to remote worldwide