The Role
The AI Systems Engineer will design AI architectures, build training pipelines, optimize models for edge inference, and manage model lifecycles.
Summary Generated by Built In
Evolved Aerospace develops mission-critical aerospace systems designed for real operational use.
We combine autonomy, software, sensing, and systems integration to deliver capability that performs in contested and constrained environments — not just in test settings.
Our work is guided by a simple principle: platforms are means, not the objective. Mission outcomes define success.
AI Systems Engineer (Edge / MLOps)
Location: Linkoping, Sweden
Team: Munin CCI
You’ll architect and deploy AI systems spanning onboard and offboard components, focusing on performance, reliability, and real-world deployment.
Responsibilities
- Design Munin’s core AI architecture
- Build training and deployment pipelines
- Implement multi-agent coordination logic
- Optimise models for real-time edge inference
- Manage model monitoring, updates, and lifecycle
- Drive synthetic data strategies
Core Skills
- Deep learning architectures
- Edge/offboard AI systems
- Model optimisation (quantisation, pruning)
- Dataset engineering
Tools & Tech
- PyTorch
- TensorRT
- ONNX
- Docker
- MLflow/W&B
If you’re driven by real-world impact, we’d like to hear from you.
Apply below or contact Connor Hird via LinkedIn or [email protected].
Evolved Aerospace is an equal opportunity employer.
Skills Required
- Experience with deep learning architectures
- Knowledge of edge/offboard AI systems
- Skills in model optimisation (quantization, pruning)
- Experience in dataset engineering
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The Company
What We Do
Evolved Aerospace develops advanced unmanned aerial mission systems designed to deliver decisive operational advantage across complex environments, focusing on autonomous, long-range capabilities for surveillance and defense.
