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Axelera AI is seeking an AI Systems Engineer to build and maintain the Wingman agentic AI platform. You will develop agent capabilities, execution environments, and software integrations that make the platform reliable and production-ready. This role spans the full lifecycle from implementation through hardware validation to production operation.
Key responsibilities include:
- Building reliable agent systems, intelligent tools, and application integrations
- Developing secure, scalable execution environments for demanding AI workloads
- Building and maintaining Wingman's platform capabilities, including access to accelerated computing and remote workload execution
- Deploying and optimizing computer vision models, LLM services, and inference infrastructure
- Owning capabilities from implementation and hardware validation through production operation
- Testing functionality, security boundaries, hardware behavior, and failure recovery
- Writing clear documentation, reproducible setup instructions, and operational runbooks
- Collaborating effectively with the team and exercising sound technical judgment
About Axelera AI: A deep-tech company founded five years ago with $370M in funding, 250+ employees (including 60+ PhDs), and offices across Belgium, France, Switzerland, Italy, the UK, and the Netherlands. The company has launched the Metis™ AI Platform, achieving 3-5x efficiency and performance improvements, with a business pipeline exceeding $100 million.
QUALIFICATIONS
Required:
- Practical experience building with AI agents, model APIs, and tools
- Understanding of how agents interact with applications, manage state, and recover from failures
- Ability to use coding agents effectively to scope, implement, and verify work while retaining ownership of correctness
- Experience with tool calling, structured outputs, streaming, or MCP
- Understanding of how AI workloads run across applications, hosts, and accelerated hardware, including remotely hosted environments
- Comfort working with Linux, processes, resource allocation, and diagnosing failures across the software and compute stack
- Application of sound security principles to permissions, credentials, and workload isolation
- Ability to build reliable execution with clear progress, cancellation, and recovery
- Hands-on experience deploying computer vision models and working with LLM services
- Ability to use representative applications and inference workloads to verify platform correctness on real hardware
- Skills to assess accuracy, latency, throughput, and resource use
- Experience investigating failures across model code, inference SDKs, and execution environments
Nice to have:
- Software engineering: Python, Rust, or another systems language; APIs, databases, native integrations; TypeScript/React and Tauri experience
- Workload orchestration: job submission, scheduling, quotas, concurrency; result retrieval and reproducible environments; SDK and driver compatibility management
- Security: authentication, authorization, least privilege; access revocation, sandboxing, network security; protection of users' files and data
- Cloud operations (Google Cloud/AWS): containers, infrastructure as code, CI/CD; monitoring, incident investigation; safe deployments, rollback, and recovery
- Applied ML & accelerated computing: PyTorch/ONNX; image/video processing, detection, classification, segmentation; calibration, quantization, and hardware-aware optimization
- Evaluation & retrieval: reproducible benchmarks for model quality, agent behavior, and task success; grounded retrieval and traceable results
- Fine-tuning, multimodal applications, distributed inference, desktop packaging, or experience maintaining developer tools
The company values demonstrated ability and judgment over particular degrees or certifications.
