AI & Optimization Engineer

About the role

We are looking for an AI & Optimization Engineer to work at the intersection of artificial intelligence, optimization, and large‑scale infrastructure operations in a globally distributed remote environment. The role focuses on turning complex operational data into intelligent systems that drive automation, predictive insights and efficiency improvements.

Key responsibilities

• Define and execute AI and optimization strategies to improve operational efficiency and business performance.
• Design and maintain scalable data models, schemas and telemetry structures for AI/ML initiatives.
• Develop, deploy and optimize machine learning models for anomaly detection, predictive maintenance, automation, performance optimization and forecasting.
• Collaborate with engineering teams to integrate AI capabilities into existing systems, APIs and production environments.
• Monitor and improve model performance, reliability, explainability and scalability in live settings.
• Translate operational challenges into data‑driven solutions and actionable insights.
• Document architectures, modelling assumptions and AI workflows while promoting a data‑driven culture.
• Stay up‑to‑date with emerging trends in AI, optimization, industrial analytics and automation.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, Engineering, Statistics or a related technical field.
• At least 3 years of experience in AI/ML engineering, applied optimization, data science or related analytical roles.
• Strong expertise in Python and modern machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit‑Learn).
• Proficiency in JavaScript or TypeScript within Node.js environments.
• Experience designing and managing large‑scale, time‑series‑focused data architectures and pipelines.
• Solid understanding of optimization techniques, predictive modelling, anomaly detection and telemetry‑based AI systems.
• Familiarity with Docker and containerised deployment environments.
• Experience deploying, monitoring and maintaining production AI systems, including handling model drift.

Required skills

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