team.blue Belgium is the Belgian division of team.blue, operating Combell (#1 Belgian hoster), OpenMinds, and other brands. Headquartered in Ghent with strong focus on managed hosting, cloud and SaaS for European SMBs. media } AI · Multiple locations · Fully Remote

Principal AI/ML Engineer

We are building the AI layer that runs across one of Europe’s largest digital-services ecosystems. Join us as Principal AI/ML Engineer and shape scalable AI that impacts millions daily.

Position Overview

team.blueis building the AI layer that runs across one of Europe’s largest digital-services ecosystems, powering hosting, domains, email, and SaaS for millions of SMBs. As Principal AI/ML Engineer you will be the senior technical authority on AI systems end-to-end: from model research and fine-tuning through agentic orchestration, real-time inference, and production reliability. This is not a research-only role and not an MLOps-only role. You will do both, setting technical direction, shipping production AI, and raising the bar across a team that is moving fast.

Key Responsibilities:

Agentic AI Systems

• Architect and evolve our multi-agent orchestration platform (currently built on Hermes / Multica), including plugin systems, tool-use pipelines, observability hooks, and channel adapters (voice, telephony, messaging)

• Design and implement voice AI pipelines – STT (VibeVoice-ASR, Whisper), real-time TTS with streaming (VibeVoice-Realtime), VAD (Silero), SIP/RTP telephony integration – with sub-300 ms end-to-end latency targets

• Build and maintain RAG pipelines with retrieval quality measurement, re-ranking, and hybrid search over vector + keyword indexes

• Define MCP server architecture and tool-use contracts across internal and third-party integrations

Model Development & Fine-Tuning

• Fine-tune and evaluate LLMs (LoRA, QLoRA, DPO) for domain-specific tasks including customer support, classification, and structured extraction

• Evaluate and benchmark model quality using automated evals, human preference data, and domain-specific metrics (WER, DER, cpWER for speech; RAGAS / LLM-as-judge for RAG)

• Manage model lifecycle: experiment tracking, versioning, reproducibility, and promotion to production

Observability & Reliability

• Own the AI observability stack: Langfuse tracing, span-level LLM call instrumentation, cost tracking, and quality regression alerting

• Define and enforce guardrails: hallucination detection, PII redaction, output safety scanning, and rate-limiting across multi-tenant deployments

Platform & Pipelines

• Build data ingestion, preprocessing, and feature pipelines supporting model training and continual learning

• Drive CI/CD for ML: automated eval gating, shadow deployments, canary releases, and rollback triggers

Technical Leadership

• Set architectural standards for AI systems across the group; conduct design reviews and own ADRs for major decisions

• Mentor ML engineers and applied scientists; grow the team’s capabilities in production AI, not just prototype AI

• Collaborate with Product and Commercial teams to translate business problems into ML problem formulations with clear success metrics

• Engage with external research partners and track emerging work (arXiv, conference proceedings, open-source releases) to identify signals worth productionizing