Artificial Intelligence Engineer

**About your role** You'll build the agent and the models behind it, from tool and skill design to fine-tuning and serving open-weight models on customer premises. **Agent engineering** * Design and build agent tools and process skills that let the agent plan scans, generate robot programs and resolve motion errors. * Build multi-model orchestration, routing tasks across large and small models to balance accuracy, latency and token cost. * Own context engineering: prompts, tool schemas, and retrieval over process knowledge. **Models** * Fine-tune open-weight large language models and vision-language models (supervised fine-tuning, Low-Rank Adaptation and its quantised variant) on domain data. * Distill large teacher models into small, deployable models. * Serve and optimise models on graphics processing units for on-premises and airgapped deployments (quantisation, batching, vLLM or similar). **Evaluation and reliability** * Build deterministic evaluation harnesses and benchmark suites; track regressions across model and agent versions. * Own model and dataset versioning, experiment tracking and release workflows. * Build logging and tracing for agent runs to support debugging and reliability. * Curate training and evaluation datasets for agent and model development. **Requirements** * 2–5 years building machine learning or large language model systems that reached production. * Strong Python and solid engineering practice (testing, Git, code review, application programming interfaces). * Hands-on agent development: tool and function calling, structured outputs, prompt and context engineering. * Hands-on fine-tuning of open-weight models (PyTorch, Hugging Face, parameter-efficient fine-tuning libraries). * Experience designing evaluations that measure model or agent quality objectively. * Experience deploying models (Docker, graphics processing unit inference, cloud platforms). **Good to have** * Multi-agent or subagent architectures. * Distillation, quantisation, or on-premises and airgapped deployment. * Vision-language models, 3D point clouds, or computer vision. * Robotics, Robot Operating System, motion planning or industrial automation. * Agile development with Jira. * A degree in computer science or a related field is welcome but not required. We care about what you've built. **What success looks like** * 3 months: shipped a new agent tool or skill to production, with an evaluation covering it. * 6 months: own a fine-tuned or distilled model in the evaluation and release pipeline. * 12 months: a self-hosted model you built is running in a customer deployment.