AI Engineer

Company Description

kuubiik is a global consulting company headquartered in Singapore, offering outsourcing and project-based solutions across all business functions in over 150 countries. With a diverse team spanning Asia, Europe, and the Americas, kuubiik has earned the trust of renowned brands like Google, TikTok, HP, and TELUS. Focused on flexibility, kuubiik offers hourly or full-time outsourcing services tailored to meet client needs. The company emphasizes collaboration and innovation, delivering customized solutions at competitive rates.

Role Overview:

• Employment: Full-Time
• Type: Permanent
• Working Hours: Monday - Friday l 9:00 AM - 5:00 PM SGT
• Location: Onsite, Singapore

Core Responsibilities

1. Predictive Model Development & Research

• Design, build, and optimize machine learning and deep learning models for anomaly detection, failure prediction, and fault diagnosis.
• Analyze complex, noisy, high-dimensional IoT time-series sensor streams (vibration, acoustic, thermal, electrical).
• Benchmark state-of-the-art research from academic literature and translate novel condition-monitoring techniques into production-ready algorithms.
• Design rigorous experiments to validate hypotheses against real-world machine failure datasets.

2. Production MLOps & System Architecture

• Deploy scalable, low-latency AI models into cloud environments (AWS, GCP, or Azure) using production-grade MLOps practices.
• Build real-time monitoring tools to track model drift, latency, and performance in live industrial settings.
• Continuously retrain and refine algorithms based on real-world feedback, test results, and edge edge-case data.

3. Cross-Functional Collaboration & Field Execution

• Work alongside engineering teams to optimize raw data pipelines and refine input features.
• Document system architectures, model performance metrics, and actionable insights for internal and external executive stakeholders.
• IoT Field Engineering Support: Assist in planning, installing, and configuring IoT hardware and software solutions at client sites when needed to ensure uninterrupted data flow and field stability.

Requirements & Qualifications

• Core Expertise: Proven track record in machine learning, complex time-series analysis, signal processing, and anomaly/failure detection.
• Programming Languages: Proficiency in Python (R or Java familiarity is a plus).
• ML Frameworks: Hands-on experience with TensorFlow, PyTorch, and Scikit-learn .
• Cloud & MLOps: Practical experience deploying AI models on cloud infrastructure ( AWS, GCP, or Azure ).
• Domain Data Knowledge: Strong background working with physical sensor data (e.g., Fourier transforms, vibration analysis, current signatures, pressure/temperature metrics).
• Exceptional problem-solving skills with a strong ability to handle noisy, incomplete, or high-dimensional datasets.
• Strong communication skills to bridge technical data science with operational client needs.
• High degree of curiosity, self-drive, and comfort working in high-velocity startup environments.
• 2+ years of experience in industrial, manufacturing, or heavy-machinery settings.
• Deep understanding of both data-driven and physics/model-based condition monitoring.
• Experience developing agentic AI systems, root-cause analysis engines, or prescriptive maintenance frameworks.