Role: AI Solution Architect - Industrial AILocation: Riyadh, KSAExperience : 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 Gen AI, AI agents, RAG, knowledge graphs or hybrid physics-ML modelling. Knowledge of MLOps platforms, drift detection and responsible AI practices.
Responsibilities / Expectations from the Role1 - 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.