Software Engineer (Agentic AI)

Wave Group; Barcelona; Hybrid; EUR 90,000 - 110,000; Full time

Job summary

Wave Group is seeking a Software Engineer (Agentic AI) to join our rapidly scaling startup. You will build and operate reliable agentic AI systems for enterprise clients, focusing on behavior, decisions, failure recovery and scalable performance.

You will own observability and evaluation layers, including tracing, logging, cost tracking, regression tests, and alerting, while working across the stack—Python services, LLM pipelines, and voice interfaces as needed.

Qualifications

• Fluent English; Spanish ideal but not required.
• Experience building backend for production-grade systems with high throughput.
• Strong background in Python backend engineering and async programming.
• Experience designing evaluation pipelines for non-deterministic systems and cost-aware model usage.
• Proven observability mindset: tracing, logging, metrics, and alerting.

Responsibilities

• Build, operate and improve reliability of agentic AI systems for enterprise clients.
• Own the observability and evaluation layer, including tracing, logging and cost tracking.
• Work across the stack: Python async services, LLM/RAG pipelines, and relevant voice interfaces.
• Design and implement production-grade deployment, monitoring, and alerting to prevent failures.
• Collaborate across teams to ensure scalable, secure, and observable solutions.

Skills

English fluency

Spanish proficiency

Python backend engineering

Observability and monitoring

CI/CD and Git

Cloud deployment (AWS/GCP)

Containerisation (Docker)

React frontend (basic)

Enterprise product experience

Model tuning / cost optimization

Non-deterministic evaluation pipelines

Tools

Celery

Redis

Docker

Twilio

React

Job description

Job Title: Software Engineer (Agentic AI)

Salary: up to ~€100k (flex for strong candidates)

+ up to 35% bonus (~20% standard, rest based on performance)

Equity: very generous, early stage equity

Location: Madrid or Barcelona (1-2 office days)

Company: autonomous AI agents for enterprise clients

About the company

This rapidly scaling start-up is building an AI-native platform that lets enterprise clients run high-volume operations through autonomous, conversational agents — voice, chat and messaging, deployed across dozens of countries in over 60 languages, for some of the largest employers in the world.

The company's core technical challenge is multi-agent orchestration that stays rock-solid at scale — reliable, secure and observable whether it's deployed for a healthcare provider in the US or a retailer in Latin America.

Founded by an experienced team with two prior successful exits, backed by a top-tier European VC, and already trusted by household-name enterprise customers.

About the role

You'll build, operate and continuously improve the reliability of the agentic AI systems running in production for enterprise clients — focused on how agents behave, decide, fail, recover and scale under real operational load, not just on shipping new features.

You'll own the observability and evaluation layer that keeps autonomous agents trustworthy at scale — tracing, logging, cost tracking across model calls, regression testing through prompt and model changes, and alerting on failures, hallucinations and degraded performance before customers ever see them.

You'll also work end-to-end across the stack: production-grade async Python services, LLM/RAG pipelines, and the conversational voice layer where relevant — STT/TTS, telephony, real-time latency.

Must have requirements

• Professional fluency in English (and ideally Spanish)
• Strong Python backend engineering — async programming, high-throughput/message-queue systems (Celery + Redis or equivalent), production-grade service design
• Experience building evaluation pipelines for non-deterministic systems — accuracy/consistency testing, regression testing across prompt and model changes, synthetic test data generation
• Production observability instincts — tracing, logging, metrics, alerting on failures and degraded performance, cost tracking and optimisation across model calls
• Cloud deployment and containerisation experience (AWS/GCP, Docker)
• Git, testing, CI/CD, code review — clean, testable, well-documented code as standard practice
• Startup pace and mentality - high energy, ownership, proactiveness, hunger to innovate and succeed, comfortable operating with ambiguity
• Conversational/voice AI experience — STT/TTS pipelines, turn-taking, telephony integration (e.g. Twilio), latency-sensitive real-time systems
• Frontend experience (ideally React)
• Experience building enterprise or B2B products
• Model fine-tuning or model-selection/cost-optimisation experience