Company
team.blueis 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.
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
Profile
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 (HuggingFace), PEFT/LoRA
Real experience with LLM inference serving — vLLM, TensorRT-LLM, or TGI — in a latency-sensitive production environment
Practical knowledge of agentic frameworks: multi-agent coordination, tool-call orchestration, context/memory management, and observability (Langfuse, Opik, or equivalent)
Experience with speech AI (ASR/TTS pipelines) or real-time audio systems is a strong plus
Solid understanding of MLOps: experiment tracking (MLflow/W&B), model registries, containerization (Docker/Kubernetes), and CI/CD for ML
Awareness of LLM-specific risk: hallucination, prompt injection, data leakage, fairness, and privacy — and how to mitigate them in production
Strong communication skills: you can write a crisp design doc, run a productive architecture review, and explain tradeoffs to a non-technical stakeholder
Nice to have
Experience with voice pipelines end-to-end: VAD → ASR → LLM → TTS → SIP/RTP telephony
Multi-hop RAG with self-consistency, chain-of-thought reranking, or RAPTOR-style hierarchical retrieval
Familiarity with MCP (Model Context Protocol) server design and tool-use contracts
Contributions to open-source ML projects or published work (arXiv, NeurIPS, ACL, Interspeech, etc.)
Experience with multimodal models (vision-language, audio-language)
Knowledge of quantization techniques (GPTQ, AWQ, GGUF) and their quality/latency tradeoffs
Right to Work
At any stage, please be prepared to provide proof of eligibility to work in the country you’re applying for. Unfortunately, we can't support relocation packages or visa sponsorship.
"Come as you are"
Everyone is welcome here. Diversity & Inclusion are at our core. Far above any technical competence, we value respect, openness, and trusted collaboration. We do not tolerate intolerance.
ESG
At , our commitment to caring for the environment and each other is at the heart of everything we do. Our latest impact report showcases our ongoing ESG efforts and ambitious sustainability goals. Interested in learning more about our dedication to making a positive impact? Check it outhere.
Originally posted on