Senior Applied AI Engineer

Modaxo; Deutschland; Sur place; EUR 110 000 - 140 000; Plein temps

Résumé du poste

Modaxo is seeking a Senior Applied AI Engineer to build the runtime, retrieval, guardrails and evaluation that make our platform’s AI features work in production, helping set the standard for a small, senior founding team.

You will focus on production integration, balancing cost and latency as core design constraints, and work closely with product teams and customers to deliver reliable AI capabilities across mission-critical transportation workflows.

Qualifications

• Production AI shipped in real production environments.
• Ability to design evaluation strategies for models and agents.
• Balance cost and latency in system design from the start.
• Clear communication with engineers, product partners and customers.
• Focus on outcomes and pragmatic shipping.

Responsabilités

• Architect and operate runtime, orchestration for production agent workflows.
• Define model selection tiers with cost and latency constraints.
• Develop evaluation frameworks and guardrails for safety.
• Own production behaviour including latency, cost, reliability.
• Collaborate with product teams and customers to validate value.
• Build scalable test and QA into the delivery pipeline.

Connaissances

Production AI

Evaluation

Cost and latency

Clear communication

Value and pragmatism

Outils

Python

C#/.NET

LLMs in production

QA automation

Description du poste

Ship AI that customers rely on every day in mission-critical workflows. These are real products, not demos.

At Modaxo, we're passionate about changing the face of people transportation. We believe that all transportation services should be safe, efficient, convenient, and equitable. Our products power mission-critical operations across a variety of industry sectors. We're making a strategic investment in AI to transform our solutions.

We are looking for an experienced, Senior Applied AI Engineer to build the runtime, retrieval, guardrails and evaluation that make our platform’s AI features work in production, and to help set the standard for a small, senior founding team.

Why This Role Is Different

• Applied, not research. We use proven commercial models and win on integration, retrieval, orchestration and safety validation. Fine-tuning is an optional track, not the goal.
• Domain-first, not tech-first. Domain knowledge, delivered through retrieval and evaluation, is the moat, not model weights. You will invest early in your customers' domain.
• Production-first. We ship AI that customers depend on daily. Demos don't count; reliability, cost and safety do.
• Platform and product. You will build reusable AI capability while enabling products to ship features fast.
• A founding role. As a founding AI hire, you will set the patterns and evaluation discipline the team adopts, and help hire the team.
• Value-based. You will validate with product teams and customers and prioritise by value, building what moves outcomes rather than what is novel.

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.

What You'll Bring

We look at demonstrated experience and scope, not years served, and we don't expect every candidate to tick every box.

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

Bonus Points

Any of these strengthen an application; none are required.

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

New to some of this? If you bring the essentials above and are excited by the problem, we'd still like to hear from you.