About the Role
Tavas is seeking an AI/ML Engineer to build the "mathematical engine" of our Microgrid EMS. You will focus on the quantitative core of our platform, using forecasting, regression and optimization algorithms to drive decision-making. You won't just be optimizing for general efficiency; your code will directly optimize for the lowest cost of energy and maximum profitability for our customers.
Key Objectives
- Pattern Recognition & Prediction: Develop models that learn complex energy production, storage, and usage patterns to generate accurate, real-time predictions.
- Financial Optimization: Implement algorithms that feed directly into the EMS to optimize battery flows and grid usage for maximum economic return.
- Edge AI Deployment: Architect pipelines that don't just live in the cloud. You will ensure your models can be deployed efficiently to NVIDIA-powered edge devices.
- Pipeline Architecture: Architect and own the end-to-end data pipeline, from raw ingestion in Google Cloud to inference on the edge.
Career Trajectory
This is a pivotal role with a clear path to leadership. You will define the roadmap for the Data Science function, set the technical standards, and will grow into the Team Lead role as the company scales.
Who You Are
- You are a Google Cloud Native, comfortable building within the GCP ecosystem (Vertex AI, BigQuery, etc.).
- You have experience with NVIDIA tools for edge computing (Jetson, TensorRT, DeepStream).
- You have a strong grasp of classical ML, time-series forecasting, and mathematical optimization.
- You have a "Builder Mentality", you enjoy the dynamic nature of a startup and want to set the standards for how we do ML, rather than following rigid corporate processes.
Qualifications
- Bachelor’s degree in Data Science, Computer Science, Mathematics or a related field.
- Minimum 2 years relevant experience.