Overview

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.

Responsibilities

• 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. Reporting to the Mining Automation Team Leader, the successful candidate will develop and implement AI-enabled workflows that improve the way mining, asset, operational, geotechnical, and technical information is processed, connected, analysed, and used for engineering decision-making.
• The role combines mining-domain understanding with artificial intelligence, automation, data engineering, computer vision, knowledge extraction, and graph-based retrieval. The Engineer will collaborate with multidisciplinary teams across WSP to develop practical, scalable, traceable, and responsible solutions for mine planning, operations, tailings and water management, asset performance, risk management, and engineering delivery.
• 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,
• 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.
• 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.
• 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.
• 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.
• Develop and integrate solutions using Azure AI services, Azure OpenAI, Azure Machine Learning, Azure AI
• 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.
• 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
• 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

Education

• Bachelor's or Master's degree in Mining Engineering, Computer Science, Data Science, Artificial Intelligence,
• Experience developing AI, machine learning, automation, data analytics, or document intelligence solutions in
• 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.
• Employment with WSP India is subject to the successful completion of a background verification (BGV) check conducted by a third-party agency appointed by WSP India.
• Candidates are advised to ensure that all information provided during the recruitment process — including documents uploaded — is accurate and complete, both to WSP India and its BGV partner.
• Passionate people doing purposeful and sustainable work that helps shape our communities and the future.
• A collaborative team that thrives on challenges and unconventional thinking.
• A network of experts channeling our curiosity into creating solutions for complex issues.
• Maximize collaboration.
• Maintain product quality and cultural integrity.
• Balance community, collaboration, opportunity, productivity, and efficiency.