Machine Learning Engineer

• Design, build and maintain end-to-end automated data pipelines from internal and external sources into a central data platform including scheduling, monitoring and resolving issues for assigned workflows:
• Platform maintenance and development of Globeleq’s Central Asset Management system (CAMs), including calculation development, on-boarding assets and equipment, and a continuous improvement focus.
• Develop, implement and extend the integrated SSOT database, consolidating data from CAMs, ERP, OT/IoT, SharePoint and other platforms according to agreed standards ensuring scalable data flows.
• Develop modular and scalable data ingestion using API integrations, SQL stored procedures and ETL frameworks, maintaining reliable, automated data ingestion between internal and external platforms to the SSOT database.
• Support and provide data development solutions. Be a practical driver and enforcer of the Data Transformation plan on behalf of the Data Engineering Manager; escalating risks and developing solutions.
• Design and develop scalable data models (staging, core, marts, feature sets) that support strategic reporting, advanced analytics and ML.
• Develop scalable data models across clearly defined business layers, including staging (raw landed data), core (cleaned and standardised single source of truth), marts (business-ready views for specific domains), and feature sets (model-ready tables for machine learning and advanced analytics).
• Develop and support workflow automation and lightweight data applications using tools such as Power Apps and Power Automate, integrating these solutions with the core data platform to enable efficient business processes.
• Develop ML algorithms, dedicated toward anomaly detection, prediction and neural networks. Ensuring all data models, pipelines and storage approaches are AI/ML-ready, including feature-ready datasets for pattern recognition, prediction and anomaly detection.
• Own end-to-end development and processing (data algorithms and ML solutions)
• Technical platform development, data orchestration and data management.
• Adhere to governing data management, security and governance standards in line with the Data Governance Policy (Data Engineering, Audit and Risk, Cyber Security and IT requirements).
• Maintain, document and track comprehensive technical documentation and change management records for architectures, pipelines, automations, environments and access.
• Work with divisional data owners to reduce data silos, standardise data flows and ensure adherence to agreed standards and timelines.

Associate Data, ML Engineer

• Executing the technical implementation of Globeleq’s Data Transformation initiative
• Designing and building the data ingestion and processing platform
• Delivering scalable, robust, production-grade data capabilities that support reliable reporting, advanced analytics and machine learning use cases
• Contributing to the development of digital management systems, O&M projects implementations, integration of new power plants, and ongoing development within Globeleq’s Data Transformation Project

Senior Data Engineer, ML

• Define and lead the architecture for scalable Machine Learning and AI platforms.
• Design end-to-end ML workflows using Databricks, including: Feature engineering, Model training, Experimentation, Deployment, Monitoring
• Architect scalable data pipelines for AI/ML workloads using:, Apache Spark, Python, SQL
• Establish MLOps best practices including:, CI/CD for ML, Model versioning, Model governance, Automated retraining, Model drifting, Observability and monitoring
• Design secure and compliant AI architectures aligned with governance and privacy standards.
• Partner with Data Engineering teams to optimize data models and feature stores.
• Guide Data Scientists and ML Engineers on scalable production design patterns.
• Evaluate and integrate modern AI capabilities, including (this will be a plus): LLMs, Vector databases, Retrieval augmented generation (RAG), AI agents
• Drive cost optimization, scalability, and operational excellence across ML platforms.
• Define reference architectures and best practices across multiple ML teams (not just owning a single project).
• Support stakeholder engagement and translate business needs into scalable technical solutions.

Machine Learning Architect

AI Engineer

• Design, develop, and deploy AI and machine learning models for real-world applications.
• Build and optimize AI-powered tools using technologies such as OpenAI, Claude, Gemini, or open-source LLMs.
• Develop scalable APIs and backend services for AI applications.
• Fine-tune machine learning models and improve model performance.
• Work with structured and unstructured datasets for training and inference.
• Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
• Integrate AI solutions into web platforms, CRMs, and business systems.
• Monitor AI model performance, reliability, and scalability in production.
• Collaborate with cross-functional teams to define AI product requirements and technical roadmaps.
• Stay updated on emerging AI technologies, frameworks, and best practices.

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Associate Data, ML Engineer

Senior Data Engineer, ML

Machine Learning Architect

AI Engineer