AI & Optimization Engineer

This is a fully remote position, open to applicants in Switzerland.

📋 Description

• Define and steer the AI strategy for MOS & MDK, emphasizing operational efficiency and quantifiable business results.

• Convert operational obstacles into data and optimization challenges that AI models can address.

• Create best-practice frameworks for scalable, production-ready AI systems.

• Develop a standardized data baseline/schema for all mining-site telemetry and events.

• Ensure data is organized, normalized, labeled, and accessible for AI/ML applications.

• Collaborate with backend engineers to enhance the data pipeline and integration standards.

• Construct and implement ML/AI and optimization models, including performance optimization, anomaly detection, predictive maintenance, energy efficiency insights, and operational automation recommendations.

• Continuously assess and enhance model performance in production environments.

• Partner with MOS & MDK engineering teams to integrate AI models into platform workflows and APIs.

• Work alongside site operations teams to validate outcomes and collect feedback.

• Align with leadership on the AI roadmap, priorities, impact tracking, and KPIs.

• Clearly document systems, architecture, and modeling assumptions.

• Foster a data-driven, efficiency-oriented culture within the Mining Software team.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Applied Mathematics, Engineering, Statistics, or a related field.

• Over 3 years of experience in AI/ML, data science, or applied optimization roles.

• Demonstrated experience in designing data models/schemas/baselines for large-scale, time-series-heavy datasets.

• Strong expertise in Python and contemporary ML frameworks (e.g., PyTorch, TensorFlow, Scikit-Learn).

• Proficient in JavaScript/TypeScript (Node.js ecosystem).

• Familiarity with containerized workloads (Docker).

• Experience in developing and deploying time-series, anomaly detection, classification, and predictive models for mechanical, electromechanical, or hardware-intensive equipment/systems in production settings.

• Comprehensive understanding of signal processing and feature extraction for sensor data (e.g., electrical, thermal, vibration, or telemetry signals).

• Solid knowledge of optimization techniques (linear/non-linear programming, simulation, decision systems, etc.).

• Experience in deploying, monitoring, and maintaining AI models in production systems, including managing model drift and adapting to changing operational conditions.

• Excellent English communication skills with a capacity for collaboration in distributed teams.

🏝️ Benefits

• Health insurance.

• Flexible work arrangements.

• Professional development opportunities.