Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.
Our client is a fast-growing European fintech company in the business spend management space. We're looking for a Senior Applied AI Engineer to design, build and ship customer-facing AI features end-to-end.
Responsibilities- Design, build and ship customer-facing AI features end-to-end, including RAG system design (chunking, embedding selection, retrieval, re-ranking) and agentic workflows.
- Ship external customer-facing LLM-based features into production, with safe production deployment practices.
- Do hands-on evaluation-pipeline and observability work: drift detection, quality monitoring.
- Reason independently about retrieval architecture using strong data fluency.
- Apply real engineering rigor to the Context Development Lifecycle (CDLC): generate, evaluate, distribute and observe the context that powers AI agents.
- Multiply the output and quality of the squad you join, and share patterns/practice beyond your immediate team so adoption compounds across the organisation.
- Embed directly into a client squad as a hands-on Individual Contributor.
Requirements:
- Python (Python-first).
- RAG system design: chunking, embedding selection, retrieval, re-ranking.
- Agentic workflows; safe production deployment.
- Proven experience shipping external customer-facing LLM-based features to production (not prototypes).
- Hands-on evaluation-pipeline and observability work: drift detection, quality monitoring.
- Data fluency to reason about retrieval architecture independently.
- Agent & harness engineering fluency — designing guardrails, context and verification layers for safe, reliable AI-assisted delivery; concrete evidence of having built/operated this kind of tooling, not just used it.
- Comfortable with the client's current daily tools: Claude Code and GitHub Copilot.
- Specific tooling such as AWS Bedrock, OpenAI APIs, or Langfuse (or comparable eval platforms).
We offer*:
- Flexible working format - remote, office-based or flexible
- A competitive salary and good compensation package
- Personalized career growth
- Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
- Active tech communities with regular knowledge sharing
- Education reimbursement
- Memorable anniversary presents
- Corporate events and team buildings
- Other location-specific benefits
*not applicable for freelancers
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