Senior Applied AI Engineer (#5633)

^{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.}

^{Nice-to-Have:}

• ^{Specific tooling such as AWS Bedrock, OpenAI APIs, or Langfuse (or comparable eval platforms).}