AI & Optimization Engineer

About the role

This position offers the chance to work at the intersection of artificial intelligence, optimization, and large‑scale infrastructure operations in a fully remote, globally distributed environment. You will turn complex operational data into intelligent systems that automate processes, provide predictive insights, and improve efficiency across high‑performance settings.

Key responsibilities

• Define and execute AI and optimization strategies that boost operational efficiency and measurable business performance.
• Design and maintain scalable data models, schemas, and telemetry structures for AI and machine‑learning initiatives.
• Develop, deploy, and fine‑tune machine‑learning models for anomaly detection, predictive maintenance, automation, performance optimization, and forecasting.
• Collaborate with engineering teams to embed AI capabilities into existing systems, APIs, and production workflows.
• Monitor and improve model performance, reliability, explainability, and scalability in live operational settings.
• Translate operational challenges into data‑driven solutions and actionable insights with cross‑functional stakeholders.
• Document architectures, modelling assumptions, and AI workflows while promoting a data‑driven culture.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, Engineering, Statistics, or a related technical field.
• Minimum 3 years of experience in AI/ML engineering, applied optimization, data science, or a similar analytical role.
• Strong understanding of optimization techniques, predictive modelling, anomaly detection, and industrial or telemetry‑based AI systems.

Required skills

• Python programming.
• Machine‑learning frameworks: PyTorch, TensorFlow, Scikit‑Learn.
• JavaScript or TypeScript within Node.js environments.
• Design and management of large‑scale, time‑series‑focused data architectures and pipelines.
• Docker and containerised deployment environments.
• Experience deploying, monitoring, and maintaining production AI systems, including handling model drift.