AI & Optimization Engineer

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

📋 Description

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

• Convert operational challenges into data and optimization issues that can be addressed by AI models.

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

• Develop a standardized data baseline/schema for all telemetry and events at mining sites.

• Ensure that data is structured, normalized, labeled, and readily accessible for AI/ML applications.

• Collaborate with backend engineers to advance 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 a production environment.

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

• Work closely with site-operations teams to validate outcomes and obtain feedback.

• 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).

• Familiar with containerized workloads (Docker).

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

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

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

• Experience with deploying, monitoring, and maintaining AI models in production systems, including managing model drift and changing operating conditions.

• Excellent English communication skills and the ability to collaborate within distributed teams.

🏝️ Benefits

• Work remotely from anywhere in the world.