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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.

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Zur Position

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.

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