AI Solution Architect - Industrial AI

Role

Must-Have Qualifications

• Designed, developed, and deployed machine learning and deep learning models for industrial challenges involving numerical, categorical, time series, image, and video data.
• Extensive Python programming experience for statistical analysis, custom algorithm development, and business insight generation using ML/DL models.
• Proficient in optimization techniques including Genetic Algorithms, Linear Programming, and Quadratic Programming.
• Skilled in data collection, exploratory analytics, data cleansing, feature selection, and model validation.
• Experience deploying AI/ML solutions using MLOps on Azure and AWS cloud platforms.
• Solid knowledge of NLP, large language models, retrieval-augmented generation, and major machine learning algorithms.
• Motivated by continuous learning and mastery of emerging AI and machine learning technologies.

Good-to-Have Qualifications

• Experience in oil and 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 and Expectations

• Develop AI/ML models for process deviation detection, equipment performance, predictive insights, yield estimation, and optimization support.
• Work with the Refinery Process SME to convert engineering logic and operational scenarios into model features, rules, and evaluation criteria.
• Perform data exploration, feature engineering, model training, validation, tuning, and explainability analysis.
• Deploy models into real-time or on-demand workflows and integrate outputs with alerts, dashboards, reports, and recommendation services.
• Implement model monitoring, versioning, retraining, drift detection, and performance reporting across the model lifecycle.
• Document assumptions, datasets, validation results, and limitations, and support user acceptance testing and production stabilization.