[Hiring] Applied AI Engineer @Nexfeld

Role Description

You will build the AI runtime, retrieval, guardrails, and evaluation that make the platform's AI features work in production - with cost, latency, and safety treated as first-class engineering constraints. We build on proven commercial models and win on integration, domain knowledge, and evaluation discipline. You'll report to the Head of AI Platform Engineering and join a small, senior founding team building the platform from the ground up. The opportunity: ship AI that customers depend on daily in mission-critical workflows - not demos.

What You'll Do

• AI Runtime and Orchestration:
  • 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.
• Evaluation, Safety and Quality:
  • Stand up evaluation harnesses and golden sets, and use them to make ship or no-ship calls and to catch regressions and drift.
  • Design and implement evaluation frameworks for model and agent quality.
  • Build guardrails and safety controls, treating retrieved content and tool outputs as untrusted.
• Production Ownership:
  • Own the production behaviour of what you ship, including latency, cost, reliability and failure modes.
  • Partner with product to decide when an agent is, and is not, the right tool.
• Delivering Value with Product and Customers:
  • Work with product teams and customers to validate results and outcomes, prioritising by value.
  • Take a value-based development approach: prove value with the smallest useful slice, then scale what works.
  • Instrument and measure impact, letting evidence rather than opinion drive what ships.
  • Build automated QA, including tests, quality gates and regression checks, into the delivery pipeline.
• Building the Team and Ways of Working:
  • Help build the team by setting evaluation and safety patterns others adopt, raising the bar in interviews and mentoring.
  • Foster a culture of technical excellence, customer obsession and pragmatic shipping.
  • Partner with product teams across the portfolio who own their own roadmaps.

Qualifications

• You have shipped software backed by LLMs or ML to real production users, beyond demos and notebooks.
• You reason about evaluation, not just prompting.
• You treat cost and latency as design constraints from the start.
• You explain your thinking clearly to engineers, product partners and customers, and bring people along.
• You focus on outcomes and value, and make pragmatic calls to ship.

Bonus Points

• Agent or RAG systems in production, and the failure modes that come with them.
• Model routing or measurable cost reduction.
• Evaluation harnesses driving decisions; LLM-as-judge calibrated against humans.
• Fine-tuning or distillation.
• Knowing when not to use an agent.
• Strength in Python or C#/.NET.
• Experience grounding AI in structured, well-modelled business data.
• System and platform design, and trade-off judgement such as one-way versus two-way doors.
• Experience building or operating enterprise-scale software, and its reliability, security and integration demands.
• You use AI coding assistants productively and supervise their output with judgement.

Worker Type

Regular

Number of Openings Available

3