Senior Applied AI Engineer

N-iX; España; Híbrido; EUR 90.000 - 120.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible working format

Remote work options

Professional development tools

Descripción de la vacante

N-iX is seeking a Senior Applied AI Engineer for a fast-growing European fintech client. You will design, build and ship customer-facing AI features end-to-end, including RAG systems and agentic workflows, with focus on safe production deployment and rigorous observability.

You will work hands-on on evaluation pipelines, data-fluent retrieval architectures, and guardrails for reliable AI delivery, embedding directly into client squads as an Individual Contributor.

Formación

• Proven experience shipping external customer-facing LLM features to production.
• Hands-on evaluation-pipeline and observability work: drift detection, quality monitoring.
• Data fluency to reason about retrieval architecture independently.
• Agent & harness engineering fluency — guardrails, context and verification layers for safe AI delivery.
• Comfortable with Claude Code and GitHub Copilot as daily tools.

Responsabilidades

• Design, build and ship customer-facing AI features end-to-end.
• RAG system design: chunking, embedding selection, retrieval, re-ranking.
• Ship production LLM-based features with safe deployment practices.
• Work on evaluation pipelines and observability: drift detection, quality monitoring.
• Reason about retrieval architecture with strong data fluency.
• Develop guardrails, context and verification layers for reliable AI delivery.
• Embed into client squads as an individual contributor.

Conocimientos

Python

RAG design

LLM deployment

Evaluation pipeline

Observability

Data fluency

Guardrails

Herramientas

Claude Code

GitHub Copilot

AWS Bedrock

OpenAI APIs

Langfuse

Descripción del empleo

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.
• 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).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing

Project: Automated claims management platform.