AI​/ML Engineer

Company team.blue is an ecosystem of 60+ successful brands working together across 22 European countries to provide its 3.3 million SMB customers with everything they need to succeed online by offering best-in-class expertise and services. team.blue's brands are a mix of traditional hosting businesses that offer services from domain names, email, shared hosting, e-commerce, and server hosting solutions and, as specialist SaaS providers, adjacent products such as compliance, marketing tools, and team collaboration products.

This broad product offering makes it a one-stop partner for online businesses and entrepreneurs across Europe. Position team.blue is 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 Solutions 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.

• 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 (Vibe Voice-ASR, Whisper), real-time TTS with streaming (Vibe Voice-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 product ionizing

• 8+ years in ML Engineering, Applied AI, or Research Engineering with at least 2 years in a lead or staff-level role
• Deep, hands-on experience with LLMs in production: fine-tuning, RLHF/DPO, prompt engineering, RAG, and tool use
• Fluent in Python and the core ML stack:
  PyTorch, Transformers (Hugging…