AI & Optimization Engineer

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

📋 Description

• Define and spearhead the AI strategy for MOS & MDK, concentrating on operational efficiency and quantifiable business outcomes.

• Convert operational challenges into data-driven and optimization issues suitable for AI model resolution.

• Develop best-practice frameworks for scalable and production-ready AI systems.

• Create a standardized data baseline/schema for telemetry and events across all mining sites.

• Ensure data is well-structured, 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 failure & maintenance, energy efficiency insights, and operational automation recommendations.

• Continuously assess and enhance model performance in live production settings.

• 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, setting priorities, tracking impact, and defining KPIs.

• Clearly document systems, architecture, and modeling assumptions.

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

• Keep abreast of trends in AI, optimization, and industrial analytics.

⛳️ 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-rich datasets.

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

• Solid proficiency in JavaScript/TypeScript (Node.js ecosystem).

• Understanding of 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 environments.

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

• Strong grasp 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 evolving operating conditions.

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

🏝️ Benefits

• Flexible working arrangements.

• Professional development opportunities.