AI Solution Architect - Industrial AI

Role: AI Solution Architect - Industrial AI Location: Riyadh, KSA Experience : 10+ Years Must-Have:- - Designed, developed, and deployed Machine Learning and Deep Learning models for industrial challenges involving large-scale numerical, categorical, and time series data, as well as images and videos. - Extensive experience in Python programming for statistical analysis, custom algorithm development, and business insight generation using ML/DL models. - Proficient in optimization techniques including Genetic Algorithms, Linear and Quadratic Programming, and others - Skilled in comprehensive data workflows: collection, exploratory analytics, data cleansing, feature selection, and model validation. - Experience in deploying AI/ML solutions using MLOPs on cloud platforms such as Azure and AWS. - Solid knowledge of NLP, Large Language Models, Retrieval-Augmented Generation, and ML algorithms such as Decision Trees, Clustering, Support Vector Machines, Artificial Neural Networks, LSTM, CNN, and YOLO, with awareness of their practical advantages and limitations. - Motivated by continuous learning and mastery of emerging technologies and methodologies in the fields of artificial intelligence and machine learning. Good-to-Have. - Experience in Oil & Gas, refinery, asset monitoring or other process industries. - Exposure to GenAI, AI agents, RAG, knowledge graphs or hybrid physics-ML modelling. - Knowledge of MLOps platforms, drift detection and responsible AI practices. Responsibilities / Expectations from the Role 1 - Develop AI/ML models for process deviation detection, equipment performance, predictive insights, yield estimation and optimization support. 2 - Work with the Refinery Process SME to convert engineering logic and operational scenarios into model features, rules and evaluation criteria. 3 - Perform data exploration, feature engineering, model training, validation, tuning and explainability analysis. 4 - Deploy models into real-time or on-demand workflows and integrate outputs with alerts, dashboards, reports and recommendation services. 5 - Implement model monitoring, versioning, retraining, drift detection and performance reporting across the model lifecycle. 6 - Document assumptions, datasets, validation results and limitations, and support user acceptance testing and production stabilization.

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