AI & Optimization Engineer

About the role

This remote position focuses on applying artificial intelligence and optimization techniques to large‑scale infrastructure operations. You will turn complex operational data into intelligent systems that automate processes, provide predictive insights, and improve efficiency across high‑performance environments.

Key responsibilities

• Define and execute AI and optimization strategies that boost operational efficiency and business performance.
• Design and maintain scalable data models, schemas, and telemetry structures for AI initiatives.
• Develop, deploy, and fine‑tune machine‑learning models for anomaly detection, predictive maintenance, automation, and performance forecasting.
• Integrate AI capabilities into existing systems, APIs, and production workflows in collaboration with engineering teams.
• Monitor model performance, reliability, explainability, and scalability in live operational settings.
• Document architectures, modelling assumptions, and AI workflows while fostering a data‑driven culture.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, Engineering, Statistics, or a related field.
• Minimum 3 years of experience in AI/ML engineering, applied optimization, or data‑science roles.
• Strong expertise in Python and modern ML frameworks (PyTorch, TensorFlow, Scikit‑Learn).
• Proficiency in JavaScript or TypeScript within Node.js environments.
• Experience with large‑scale, time‑series data architectures and pipelines.
• Solid understanding of optimization techniques, predictive modelling, and anomaly detection.
• Familiarity with Docker and containerised deployment.

Required skills

• Python
• PyTorch
• TensorFlow
• Scikit‑Learn
• JavaScript
• TypeScript
• Node.js
• Docker
• Time‑series data pipelines
• Optimization techniques
• Predictive modelling
• Anomaly detection

What we offer

• Opportunity to work on cutting‑edge AI and telemetry technologies.
• Fully remote, globally distributed team environment.
• Collaborative culture with cross‑functional engineering and data teams.