Freelance CFD Engineer training AI agents through CAD - based fluid simulations and verified reference solutions. Supporting a global platform with Python validation checks.
About the role
- Applied AI Engineer building production LLM runtime, retrieval, guardrails, and evaluation. Enabling reliable, cost-efficient AI features for mission-critical customer workflows.
Responsibilities
- Build and operate the runtime, tool calling and orchestration for production agent workflows
- Design model selection and tiering with cost and latency as first-class constraints
- Implement and tune retrieval (RAG) and grounding where it measurably improves quality
- Stand up evaluation harnesses and golden sets to make ship or no-ship calls and catch regressions and drift
- Design and implement evaluation frameworks for model and agent quality
- Build guardrails and safety controls for retrieved content and tool outputs
- Own production behaviour, including latency, cost, reliability and failure modes
- Partner with product to decide when an agent is or is not the right tool
- Work with product teams and customers to validate results and outcomes
- Prove value with the smallest useful slice, then scale what works
- Instrument and measure impact so evidence drives what ships
- Build automated QA, including tests, quality gates and regression checks, into the delivery pipeline
- Set evaluation and safety patterns, contribute to interviews and mentor team members
- Foster technical excellence, customer obsession and pragmatic shipping
- Partner with product teams across the portfolio and their roadmaps
- Report to the Head of AI Platform Engineering
- Help hire the team and establish engineering patterns as a founding AI hire
Requirements
- Demonstrated experience shipping software backed by LLMs or ML to real production users, beyond demos and notebooks
- Ability to reason about evaluation, not just prompting
- Treat cost and latency as design constraints from the start
- Clear communication with engineers, product partners and customers
- Focus on outcomes and value, with pragmatic decisions to ship
- Bonus: Agent or RAG systems in production
- Bonus: Model routing or measurable cost reduction
- Bonus: Evaluation harnesses driving decisions; LLM-as-judge calibrated against humans
- Bonus: Fine-tuning or distillation
- Bonus: Knowledge of when not to use an agent
- Bonus: Strength in Python or C#/.NET
- Bonus: Experience grounding AI in structured, well-modelled business data
- Bonus: System and platform design, and trade-off judgement
- Bonus: Experience building or operating enterprise-scale software, including reliability, security and integration demands
- Bonus: Productive use of AI coding assistants with judgement when supervising output
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Degree requirement
No Education Requirement
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