Engineering Manager (AI)

Wave Group; Madrid; Híbrido; EUR 100.000 - 130.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity up to €80k

Bonus up to 35%

Descripción de la vacante

Wave Group seeks a senior engineering leader to own architecture, reliability and scale for a production AI platform. You will write and review code while guiding a growing AI engineering team in Madrid or Barcelona.

Expect a hands‑on leadership role with hiring, career development and performance conversations, plus influence on tech strategy, architecture and delivery in a fast‑paced startup.

Formación

• Deep backend engineering in Python (async/ASGI, Django/Channels, Celery + RabbitMQ/Redis)
• Experience building high‑RPS, distributed production systems
• Proficient with SQL/NoSQL, cloud infra, CI/CD, observability
• Strong knowledge of LLMs, RAG, and agentic AI systems
• Hands‑on leadership—coding while managing teams
• People management with 5–6 direct reports, salary & performance discussions
• Startup pace, ownership, proactive problem solving, fast iterations
• Exposure to conversational/voice AI infra (Twilio/IVR, STT/TTS)

Responsabilidades

• Own architecture, reliability and scale for a production platform while staying hands-on
• Lead and grow the AI Engineering team from ~9 to ~13 engineers
• Hire, coach, conduct performance conversations and align compensation with success

Conocimientos

Python backend

Distributed systems

SQL/NoSQL

Cloud infra

CI/CD

LLMs / RAG

Hands-on coding

People management

Startup mindset

Conversational AI infra

Herramientas

Django

Channels

Celery

RabbitMQ

Redis

Twilio

Descripción del empleo

Salary: €100-130k + up to 35% bonus (~20% guaranteed, rest based on performance)

Equity: up to ~€80k

Location: Madrid (preferred) 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 be a senior engineering leader working directly under the CTO/Co-Founder, with genuine 50/50 weight across technical ownership and people management:

• On the technical side, you’ll own architecture, reliability and scale for a production platform handling real-time, high-throughput traffic, whilst staying hands-on (writing, reviewing, debugging code).
• On the management side, you’ll be growing and running a high-performing AI Engineering team, scaling from ~9 to ~13 in the near future. This role includes hiring, coaching, career development, performance conversations, comp discussions etc. — not just technical leadership.

Must have requirements:

• Deep backend engineering in Python — async/ASGI, Django/Channels, Celery + RabbitMQ/Redis
• Proven experience building and operating high-RPS, distributed, production-grade systems
• Comfort with SQL/NoSQL, cloud infra, CI/CD and production observability
• Strong working knowledge of LLMs, RAG, and agentic AI systems (you don't need to have built the agents yourself, but you understand how they work and what breaks them)
• A genuine "still in the code" mindset — you’ve led teams without stepping away from building
• Genuine people-management track record - at least 5-6 direct reports, comfortable owning performance management, salary/comp conversations and career development, not just technical leadership
• Startup pace and mentality - high energy, ownership, proactiveness, hunger to innovate and succeed
• Exposure to conversational/voice AI infrastructure (Twilio/IVR, STT/TTS, latency optimisation)