Senior AI Engineer

LearnWise AI; Barcelona; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

LLM gateways

Multi-region deployments

Self-hosted embeddings

Observability and evals

Data residency compliance

Job summary

LearnWise AI is hiring a senior AI engineer to own backend systems for agents, API routing, and in-region inference. You will ship AI product features from design to production and ensure reliability across integrations with LMS platforms and institutional systems.

You will lead design reviews, champion observability, and help extend the integration framework for SIS, CRM, and ticketing tools. Strong backend experience and curiosity about async Python are required.

Qualifications

• 6+ years building production backend services
• Experience with multiple backend languages
• Python knowledge with interest in async Python
• Experience shipping LLM features: tool-calling, structured outputs, streaming, RAG, embeddings
• Reliability with third-party dependencies: retries, timeouts, fallbacks

Responsibilities

• Design and implement agents that integrate with LMS APIs and audit trails
• Build and maintain the AI platform including routing and in-region inference
• Ship AI product features from design to production
• Make AI observable with monitoring and feedback loops
• Extend the integration framework for SIS, CRM, and ticketing systems
• Write designs before code and review peers' designs

Skills

Backend engineering

Python

Go

TypeScript/Node.js

Java

Rust

PHP

English communication

Tools

Docker

CI/CD tooling

AWS

OpenTelemetry

LLM tooling / embeddings

Job description

LearnWise AI is an AI-first scale-up building virtual assistants, instructor efficiency tools, and student engagement solutions for universities and colleges. We're a small team moving fast: the best idea wins no matter who says it, and every hire owns real scope from day one.

About LearnWise

LearnWise AI is an AI-first scale-up building virtual assistants, instructor efficiency tools, and student engagement solutions for universities and colleges. We're a small team moving fast: the best idea wins no matter who says it, and every hire owns real scope from day one.

About the role

LearnWise builds AI products for higher education that run inside Canvas, Brightspace, Moodle, and Blackboard. We are hiring a senior AI engineer who owns the systems underneath these products: agents that act safely on real systems, how we call and route models, where inference runs, and what it costs. You design it, write it down, ship it behind safe rollouts, and keep it reliable in production. This is production engineering around LLMs and agents, not model training.

What you'll do

Build agents that act on real systems

• Design how agents call LMS and institutional APIs, with permissions, human approvals, sandboxed execution, and audit.

Build the AI platform

• Model routing, provider fallbacks, in-region inference, usage limits, and cost control.

Ship AI product features

• Take new learning, admin, and multichannel experiences from design to production.

Make AI observable and self-improving

• Detect silent failures, measure quality, and feed fixes back into prompts, workflows, and agent logic.

Extend the integration platform

• Grow the framework that connects SIS, CRM, ticketing, and other institutional systems.

Write designs before code

• Propose, align, then build; review others' designs and code.

What you bring

• 6+ years as a software engineer building production backend services.
• Production experience in more than one backend language (e.g., Python, Go, TypeScript/Node, Java, Rust, PHP).
• Working Python knowledge and interest in learning async Python.
• LLM features shipped to production: tool-calling, structured outputs, streaming, RAG, embeddings, and their failure modes.
• Reliability under third-party dependency: retries, timeouts, fallbacks, rate limits, provider outages.
• You deploy what you build: containers, CI/CD, and a cloud platform (AWS preferred).
• Clear, direct written and spoken English; you raise blockers early.
• Daily use of AI coding tools, e.g., Codex or Claude, and you verify what they produce.
• Technical leadership: led development of a larger project or system across several engineers.
• Architecture design of services or AI platform components that others built on.

Nice to have

• AI Ops or agentic workflows: agents that call external APIs and take actions, with guardrails.
• LLM gateways or routers (LiteLLM or similar) and multi-provider setups (OpenAI, Anthropic, Bedrock, Azure OpenAI, Gemini).
• Retrieval at scale, including self-hosted embedding models.
• Observability and evals for AI systems (OpenTelemetry, Logfire or similar).
• Data residency and multi-region deployments (GDPR, FERPA).