AI/ML Senior Engineer

About the job

Responsibilities: • Lead the architecture and development of AI/ML models for HVAC fault detection, targeting 95%+ accuracy on the top 25 fault types. • Design and implement weak supervision strategies using rule-based "Silver Labels" and technician-verified "Gold Labels". • Build and optimise classification models (Random Forest, XGBoost, neural networks) for time-series anomaly detection. • Develop model interpretability features that explain fault predictions with underlying decision data. • Establish data normalisation and segmentation strategies for environmental conditions (temperature, humidity profiles). • Design human-in-the-loop feedback systems for continuous model improvement. • Collaborate with data engineers on data pipeline requirements and feature engineering. • Define model evaluation metrics, conduct failure analysis, and iterate on model performance. • Mentor mid-level ML engineers and establish best practices for ML development. • Stay current with SOTA approaches in time-series analysis, anomaly detection, and industrial AI Requirements: • 6+ years of professional experience in machine learning engineering, with 3+ years in a senior or lead role. • Strong expertise in Python and ML frameworks (scikit-learn, XGBoost, PyTorch/TensorFlow). • Proven experience with time-series analysis, anomaly detection, or predictive maintenance systems. • Deep understanding of supervised, unsupervised, and weak supervision techniques. • Experience with feature engineering on sensor/telemetry data. • Solid understanding of model interpretability and explainable AI techniques. • Experience deploying ML models to production environments. • Excellent communication skills to translate technical concepts for cross-functional teams. • Strong problem-solving skills and ability to work independently. • Experience with IoT data pipelines and industrial equipment monitoring. • Familiarity with Azure Machine Learning, Azure Databricks, or similar cloud ML platforms. • Background in HVAC systems, building automation, or the mechanical engineering domain. • Experience with time-series databases (InfluxDB, TimescaleDB). • Publications or contributions to ML/AI research. • Experience with RLHF or reinforcement learning from human feedback systems.

Requirements

• Python
• Machine Learning
• HVAC Systems
• Time-Series Analysis
• Anomaly Detection

Qualifications

• 6+ years in machine learning engineering
• 3+ years in a senior role

Preferred Technologies

• Python
• Machine Learning
• HVAC Systems
• Time-Series Analysis
• Anomaly Detection

About the company

AVJ Technologies focuses on artificial intelligence and machine learning solutions, particularly in the area of HVAC fault detection.