MLOps Engineer

Enerzyz is an AI-powered, protocol-agnostic energy asset orchestration platform. It sits on top of existing control infrastructure and autonomously runs energy assets across data centres, commercial buildings, and industrial sites .

Our models don’t just predict. They act on physical equipment.

The Role

As an MLOps Engineer at Enerzyz , you’ll own the path from raw sensor data to models running safely in live facilities .

That covers ingestion, training, deployment across cloud and edge environments, monitoring, and recovery.

Here, a degraded model can mean real equipment behaving badly — so reliability is the whole job.

What You’ll Do

• Build and run ML pipelines for forecasting, optimisation, fault detection, and asset health models
• Deploy and serve models across cloud and edge environments with versioning, rollback, and graceful fallback
• Build data validation systems for noisy, gappy, multi-vendor time-series data
• Set up shadow mode, champion–challenger testing, and staged rollouts before models touch control
• Monitor production for drift, degradation, and savings performance across many sites
• Work with controls and QA engineers to enforce safe operating constraints on model outputs

What You Bring

• Proven experience in MLOps, ML infrastructure, or data engineering , with models running in production
• Strong experience with time-series or sensor data at scale
• Proficiency in Python, Docker, Kubernetes , and at least one major cloud platform
• Experience with ML tooling such as MLflow, Kubeflow, Airflow, or similar
• Experience with CI/CD for ML
• Experience deploying models to edge or resource-constrained environments

Nice to Have

• Background in energy, industrial, IoT, or building technology
• Familiarity with industrial protocols such as BACnet, Modbus, or OPC-UA
• Experience with time-series databases
• Experience with optimisation, reinforcement learning, or physics-informed models
• Exposure to LLM deployment and retrieval-based systems