Job Title:
AI Engineer – Energy Modeling & Optimization
Location:
Menen, West Flanders, Belgium (Hybrid)
About FIRN Energy
FIRN Energy is a clean tech innovator committed to reducing energy costs and accelerating the transition to sustainable energy. Founded by Frank Degezelle and Nico Ramacker, we deliver tailored energy solutions — including solar PV, wind energy, battery storage, EV charging infrastructure, and AI-driven optimization tools.
We've developed our own in-house software platform that combines IoT, real-time analytics, and smart control to help individuals, farms, and businesses take control of their energy performance. We're building the tools that are shaping tomorrow's energy landscape — and we want you on board.
Role Overview
As our new
AI Engineer – Energy Modeling & Optimization
, you will be responsible for designing, building, and improving the algorithms that drive FIRN's smart energy decisions.
You will work on forecasting, simulation, and optimization models for solar, batteries, EV chargers, dynamic tariffs, and flexible loads. Together with our EMS and software teams, you'll turn data into actionable control strategies that run in real time on our platform and on-site controllers.
This role is ideal for someone who loves coding, math, and energy systems — and wants to see their models deployed in real industrial and commercial projects.
Responsibilities
* Develop and improve
forecasting models
for solar production, consumption, market prices, and flexibility.
* Build
optimization algorithms
for:
* Battery charging/discharging
* Curtailment strategies
* Peak shaving and self-consumption
* Dynamic tariffs and market participation
* Design and maintain
simulation tools and digital twins
for sites with PV, storage, EV charging, and flexible loads.
* Translate business and engineering requirements into
data-driven models
that can run in production (real-time or near real-time).
* Work closely with the EMS, backend, and project teams to
integrate models
into FIRN's platform and controller.
* Validate model performance using real-world data, run A/B tests and improve models based on field results.
* Help define and implement
MLOps / DataOps practices
(versioning, monitoring, retraining pipelines).
* Contribute to FIRN's longer-term
AI roadmap
: new use cases, new algorithms, and product features.
Your Profile
* Master's degree (or strong equivalent experience) in
Computer Science, Applied Mathematics, AI, Data Science, Engineering
, or similar.
* Solid experience with
Python
and common data/ML libraries (e.g. NumPy, pandas, scikit-learn, PyTorch or TensorFlow).
* Experience with one or more of the following:
* Time-series modeling
and forecasting
* Optimization
(e.g. linear/non-linear optimization, MILP, heuristics, reinforcement learning)
* Control algorithms
for physical systems
* Bonus points if you have experience in:
* Energy systems (solar PV, batteries, EV charging, HVAC, industrial loads)
* Working with IoT / sensor data
* Building and deploying models in production (APIs, containers, edge devices)
* Strong analytical mindset; you enjoy experimenting, validating, and iterating.
* Fluent in
Dutch
and comfortable in
English
(written and spoken).
* Familiarity with Git-based workflows and modern development practices; experience with tools like GitLab, Jira, or similar is a plus.
What We Offer
* A key technical role in a
clean tech scale-up
shaping the future of energy.
* The opportunity to see your models
deployed on real sites
(factories, farms, commercial buildings).
* A
hybrid work model
(Menen + remote).
* Flexible hours and competitive compensation.
* A mission-driven, no-nonsense culture where your ideas and experiments really matter.
* Plenty of room for
personal and professional growth
in AI, optimization, and energy systems.
Apply Now
Send your CV, GitHub/portfolio (if available), and a short motivation to
.
Tell us briefly about:
* A modeling or optimization project you're proud of, and
* Why working on real-world energy systems appeals to you.
Have questions? Reach out — we'd love to connect.