Engineer - AI (Mining Automation)

Responsibilities

• AI and Mining Automation Development • Design, develop, and implement AI-enabled automation solutions for mining engineering and operational workflows. • Work with mining specialists to identify high-value automation opportunities and convert technical requirements into practical digital solutions. • Develop intelligent workflows that integrate geological, geotechnical, mine planning, operational, asset, environmental, and risk information. • Support the use of generative AI, machine learning, computer vision, and agent-based approaches to improve engineering productivity and decision support. • Create reusable components and scalable solution patterns that can be applied across global mining projects. Data Extraction and Document Intelligence • Develop automated pipelines to extract information from mining reports, PDFs, scanned documents, drawings, tables, figures, inspection records, and monitoring outputs. • Apply OCR, layout understanding, multimodal models, and LLM-based techniques to identify entities, attributes, relationships, measurements, risks, and supporting evidence. • Implement validation, normalization, exception-handling, and quality-control logic to improve the accuracy and consistency of extracted information. • Maintain traceability between extracted outputs and the relevant source documents, pages, figures, tables, or evidence.
• AI Models and Advanced Analytics • Develop, validate, and support deployment of machine learning solutions for predictive analytics, anomaly detection, classification, risk assessment, optimization, and asset insights. • Apply data science and statistical methods to identify patterns, trends, and actionable insights from engineering and operational data. • Evaluate model performance, limitations, and suitability for mining use cases, working with domain experts to confirm engineering relevance. • Contribute to proof-of-concept development and the transition of successful prototypes into maintainable production solutions.
• Knowledge Management and Graph-Based Intelligence • Support the design of structured knowledge models that connect mining assets, technical information, operational data, risks, controls, and engineering evidence. • Contribute to knowledge graph and semantic data solutions using RDF, OWL, SPARQL, or equivalent technologies where appropriate. • Develop graph-native and hybrid retrieval workflows, including GraphRAG approaches that combine structured graph context with AI models. • Enable natural-language access to connected mining knowledge for engineering assistants, analytics tools, and decision-support applications. • Support provenance, versioning, evidence linking, and auditability of AI-generated or graph-derived outputs. Cloud, Data, and Platform Integration • Develop and integrate solutions using Azure AI services, Azure OpenAI, Azure Machine Learning, Azure AI Document Intelligence, and related cloud capabilities, where applicable. • Build and maintain data pipelines, APIs, connectors, and services that integrate AI workflows with engineering platforms and enterprise data environments. • Contribute to deployment, monitoring, documentation, testing, and continuous improvement of production AI applications. • Support MLOps, prompt and model evaluation, access control, data security, and responsible AI practices. Global Project Delivery and Collaboration • Work as part of the GCC Specialist Mining Services team to support multidisciplinary mining assignments delivered across WSP's global business. • Collaborate with the Mining Automation Team Leader, mining engineers, geotechnical specialists, tailings practitioners, data scientists, software developers, and digital teams. • Engage with project and domain teams to understand technical context, refine use cases, review outputs, and align solutions with project requirements. • Communicate technical concepts clearly to both technical and non-technical stakeholders across different regions and time zones. • Prepare solution documentation, specifications, test records, user guidance, and knowledge-transfer materials. • Contribute to internal standards, reusable assets, lessons learned, innovation initiatives, and capability development within the team.