AI/ ML Engineer

Technologies we use

About the project

This is how we organize our work

This is how we work

Your responsibilities

• develop algorithms for intelligent battery energy storage control
• research and evaluate different approaches to energy optimization problems
• train, evaluate, and improve Reinforcement Learning models
• develop optimization models and use them as benchmarks for RL-based solutions
• define reward functions, states, actions, constraints, and decision spaces for RL environments
• develop and improve simulations of batteries, PV installations, and energy flows
• prepare and process data related to energy consumption, PV production, weather, and electricity prices
• compare ML/RL models against baseline control strategies and optimization-based solutions
• analyze experimental results and improve models based on their real-world behavior
• validate models across different installation configurations and consumption profiles
• occasionally maintain or develop electricity price forecasting models for the day-ahead market

Our requirements

• very good knowledge of Python
• strong experience with PyTorch or another deep learning framework such as TensorFlow
• very good knowledge of pandas and NumPy
• experience with data visualization using Matplotlib, Plotly, Seaborn, or similar tools
• hands-on experience training, evaluating, and comparing Machine Learning models
• practical experience deploying ML models into production environments
• solid understanding of Reinforcement Learning and hands-on experience with at least one RL algorithm, such as DQN or PPO
• ability to define reward functions, states, actions, and constraints for RL environments
• understanding of the fundamentals of mathematical optimization
• ability to work with optimization problems combining discrete and continuous decisions
• strong understanding of model evaluation, experimentation, benchmarking, and interpretation of results
• ability to write clean, maintainable, and testable Python code
• experience with Git
• high level of independence and ownership, including the ability to define and prioritize the next steps of the project
• English at B2 level or higher

Optional

• knowledge of energy systems, renewable energy, battery storage, or energy markets
• experience with Gymnasium or TorchRL
• experience with linear programming, MILP, or dynamic programming
• experience with time-series forecasting
• practical experience preparing and analyzing time-series datasets
• knowledge of neural network architectures for forecasting, such as TFT or TiDE
• experience with Optuna, DVC, or MLflow
• experience working with weather, renewable energy production, energy consumption, or electricity price data
• experience using AI coding assistants such as Claude Code or Codex
• experience with Docker and/or Kubernetes

What we offer

• A chance to work on diverse, high-impact AI/ML projects (IoT, energy, automation, GenAI),
• Freedom to experiment and bring research ideas into production,
• Collaborative, research-driven environment,
• Long-term cooperation with growth paths toward AI Research, MLOps, or Tech Leadership.

Benefits