Senior AI/ML Engineer

About this role

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 endto-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.