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.