Description
WSP is seeking an Engineer AI (Mining Automation) to join the GCC Specialist Mining Services team, supporting the delivery of digital and automation solutions for global mining projects.
The role combines mining-domain understanding with artificial intelligence, automation, data engineering, computer vision, knowledge extraction, and graph-based retrieval.
Responsibilities
AI and Mining Automation Development
- Design, develop, and implement AI-enabled automation solutions for mining engineering and operational workflows.
- Collaborate with mining specialists to identify high-value automation opportunities and convert technical requirements into practical digital solutions.
- Develop intelligent workflows integrating 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 for 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 extracted information accuracy and consistency.
- Maintain traceability between extracted outputs and source documents.
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.
- Contribute to proof-of-concept development and transition of successful prototypes into maintainable production solutions.
Knowledge Management and Graph-Based Intelligence
- Support the design of structured knowledge models connecting 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.
- Develop graph-native and hybrid retrieval workflows, including GraphRAG approaches.
- 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, and related cloud capabilities.
- Build and maintain data pipelines, APIs, connectors, and services integrating 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.
- 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.
Qualifications
Education
- Bachelor's or Master's degree in Mining Engineering, Computer Science, Data Science, Artificial Intelligence, Software Engineering, Geotechnical Engineering, or a related discipline.
Technical Experience and Skills
- Experience developing AI, machine learning, automation, data analytics, or document intelligence solutions in mining, engineering, infrastructure, industrial, or asset-intensive environments.
- Proficiency in Python and experience with relevant AI, data science, or machine learning libraries and frameworks.
- Hands-on experience with large language models, prompt engineering, retrieval-augmented generation, or generative AI application development.
- Knowledge of data engineering concepts, ETL or ELT pipelines, APIs, structured and unstructured data processing, and system integration.
- Experience with OCR, document processing, computer vision, layout analysis, or multimodal AI techniques.
- Familiarity with Microsoft Azure-based AI and data services.