Senior 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. 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