AI Engineer

• Design solution architecture using off-the-shelf models, RAG, agents and custom models based on client problems and budgets.
• Integrate LLMs into client systems, including prompt design, tool use and agentic workflows.
• Train, validate and deploy machine learning models for demand forecasting, anomaly detection and similar problems.
• Analyse client data to assess use-case feasibility and build supporting data pipelines.
• Evaluate and monitor production solutions for accuracy, drift, latency and cost, and optimise them.
• Write tested, secure code with safeguards against prompt injection and data leakage.
• Work with client executives, business users and IT teams to translate business problems into technical requirements and contribute to proposals.
• Hand over solutions with documentation and training so client teams can operate and maintain them.
• Work directly with the founder and client teams from scoping through go-live and handover.

• 5+ years of professional software engineering, including at least two years building AI applications that run in production.
• Consulting or professional services experience, delivering to external clients against a fixed scope.
• A bachelor's or master's degree in computer science, engineering, mathematics or physics, or equivalent practical experience.
• Advanced Python, including testing, packaging and writing code that other engineers can maintain.
• Daily use of AI engineering tools such as Claude Code or Codex, including reviewing, testing and correcting the code they generate.
• Hands-on experience integrating LLMs into applications, including RAG, prompt design, tool use and evaluation.
• Experience building agents with the Model Context Protocol (MCP) or agent frameworks.
• Experience training and deploying machine learning models with libraries such as scikit-learn, PyTorch or XGBoost.
• Strong SQL and experience building data pipelines.
• Experience deploying solutions on at least one cloud platform, preferably Azure or AWS, using containers and CI/CD.
• The ability to explain technical trade-offs to non-technical executives in plain language.
• Desired (not required): Experience with data platforms such as Databricks, Snowflake or Microsoft Fabric, and transformation tools such as dbt.
• Desired (not required): Experience with LLM evaluation and observability tools, such as MLflow, Langfuse or LangSmith.
• Desired (not required): Knowledge of LLM security risks, such as those in the OWASP Top 10 for LLM Applications.
• Desired (not required): Experience running training sessions or workshops for technical or business audiences.

• Observing AU working hours
• Observing AU holidays