EnliteAI

Electrical Engineer

• Shape the architecture of larger repositories, including module boundaries, interfaces, testing strategy and maintainability decisions
• Build and harden the deployment stack using containerised services on Kubernetes, Helm, Redis and related components
• Own the demo and proof-of-concept path, enabling isolated instances with seeded data to be stood up within hours
• Operate the Kubernetes cluster on company-owned hardware, including node lifecycle, GPU enablement, storage, networking, capacity planning and upgrades
• Build infrastructure beneath data pipelines, including MQTT and Kafka brokers, topic and retention design, orchestration, storage, backend services and APIs
• Build and harden CI/CD, developer tooling and the internal platform used by ML engineers and researchers
• Collaborate with ML engineers, researchers and power systems experts on industrial and EU-funded projects

Platform & Systems Engineer – Power Systems

• Develop machine learning and reinforcement learning methods for grid operation problems
• Build learned and hybrid state estimators, surrogate models, graph neural networks, forecasting methods and reinforcement learning controls
• Review and assess state-of-the-art research for industrial-scale implementation
• Design and run experiments with baselines, ablations and realistic data regimes
• Validate methods against pandapower, PyPSA, OpenDSS and Grid2Op
• Contribute to core Maze components and extend the framework for energy and infrastructure applications
• Own model-facing data pipelines, including domain transforms, unit and sign conventions, features, train/serve consistency and schema contracts
• Take prototypes through MVP and production transition
• Contribute to deliverables and publications in EU-funded research projects

Platform & Systems Engineer – Power Systems

ML Research Engineer – Power Systems