About the Role:

Lead the development of our ML platform and complicated subsystems including optimization engines, forecasting models, and real-time data pipelines. You'll build the intelligence layer that differentiates our platform from basic SCADA systems.

Key Responsibilities:

Design and implement ML serving infrastructure for forecasting models

Build optimization engine integration

Develop real-time data pipeline for streaming analytics

Create feature store and model versioning system

Ensure ML models can be deployed and scaled efficiently

Collaborate with domain teams to identify ML opportunities

Required Qualifications:

5+ years Python development experience

Experience deploying ML models to production (not just training them)

Strong DevOps skills: Docker, Kubernetes, CI/CD pipelines

Experience with ML serving frameworks (MLflow, KubeFlow, or similar)

Understanding of time-series forecasting and optimization problems

Additional:

Energy industry experience (load forecasting, price prediction)

Experience with mathematical optimization libraries

GPU optimization and deployment experience

Experience with edge computing/IoT deployments

Experience with streaming data systems (Kafka, Pulsar, or similar)