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 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