AI Automation Engineer

Job description

the employer is recruiting for a well-established retail group in Saudi Arabia looking to hire a skilled AI Automation Engineer. The ideal candidate will be responsible for designing, developing, deploying, and supporting AI, RPA, and intelligent automation solutions to streamline business processes and improve operational efficiency across the organization. Key Responsibilities: 1. RPA & Intelligent Automation - Develop, implement, and support RPA initiatives from process assessment and solution design through development, testing, deployment, hypercare, and production support. - Build and maintain attended and unattended automations using enterprise RPA platforms such as the employer, Microsoft Power Automate, Automation Anywhere, or equivalent. - Translate business process maps and Standard Operating Procedures (SOPs) into robust automation workflows, reusable components, exception logic, and orchestration schedules. - Integrate RPA bots with ERP systems, web applications, databases, APIs, file repositories, email platforms, OCR/document processing solutions, and other enterprise applications. - Design resilient automation solutions using queues, retries, exception handling, audit trails, credential management, monitoring, alerts, and recovery procedures. - Monitor and measure automation performance, including straight-through processing, automation success rate, manual exception rate, cycle-time reduction, and FTE/capacity benefits. 2. AI-Enabled Automation & Solution Development - Combine RPA with AI/ML, OCR, document intelligence, classification, data extraction, LLMs, and decision services to improve business process automation. - Develop production-grade Python services, scripts, APIs, integration components, and lightweight applications that extend or complement RPA solutions. - Build human-in-the-loop workflows for approvals, reviews, low-confidence cases, and controlled decision-making. - Develop reusable automation components and patterns that can be scaled across Finance, Commercial, Operations, HR, Supply Chain, and other functions. - Support the practical integration of GenAI and LLM capabilities into enterprise workflows and automation solutions. 3. Deployment, Testing & Production Operations - Develop automated testing and validation approaches for bots, APIs, workflows, and AI enabled automation components. - Implement Git, version control, CI/CD, and structured software development practices for automation assets and supporting code. - Set up and maintain production monitoring, logging, alerting, dashboards, bot schedules, and operational runbooks. - Troubleshoot production incidents, perform root-cause analysis, and implement permanent corrective actions. - Ensure automation solutions comply with enterprise security, access control, segregation of-duties, audit, and data protection requirements. 4. Process Discovery & Automation Design - Work closely with business process owners to assess automation feasibility, process stability, exception patterns, data availability, risks, and expected business benefits. - Identify inefficient processes and redesign workflows where required rather than simply automating existing manual activities. - Document current-state and future-state process flows, automation requirements, exception paths, dependencies, controls, and acceptance criteria. - Support prioritization of the automation backlog based on business value, feasibility, complexity, risk, and reusability. 5. Collaboration & Knowledge Transfer - Partner with Finance, Operations, Commercial, HR, IT, Data, Security teams, and external technology vendors to deliver end-to-end automation solutions. - Work closely with the Technology & Data Lead / Senior Manager - AI Projects on architecture, integration, code quality, and production-readiness standards. - Prepare technical documentation, support knowledge transfer, and enable internal teams to operate and maintain deployed automations. - Contribute to AI CoE standards

Requirements

• Machine learning and deep learning algorithms
• Python programming
• TensorFlow / PyTorch proficiency
• Data preprocessing and feature engineering
• Model deployment and monitoring on cloud platforms

Skills