Senior AI/ML Engineer

h3Company /h3 pteam.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. /p pteam.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. /p h3Position /h3 pteam.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. /p h3Responsibilities /h3 pAgentic AI Systems /p ul li pArchitect 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) /p /li /ul ul li pDesign 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 /p /li /ul ul li pBuild and maintain RAG pipelines with retrieval quality measurement, re-ranking, and hybrid search over vector + keyword indexes /p /li /ul ul li pDefine MCP server architecture and tool-use contracts across internal and third-party integrations /p /li /ul pModel Development Fine-Tuning /p ul li pFine-tune and evaluate LLMs (LoRA, QLoRA, DPO) for domain-specific tasks including customer support, classification, and structured extraction /p /li /ul ul li pEvaluate 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) /p /li /ul ul li pManage model lifecycle: experiment tracking, versioning, reproducibility, and promotion to production /p /li /ul pObservability Reliability /p ul li pOwn the AI observability stack: Langfuse tracing, span-level LLM call instrumentation, cost tracking, and quality regression alerting /p /li /ul ul li pDefine and enforce guardrails: hallucination detection, PII redaction, output safety scanning, and rate-limiting across multi-tenant deployments /p /li /ul pPlatform Pipelines /p ul li pBuild data ingestion, preprocessing, and feature pipelines supporting model training and continual learning /p /li /ul ul li pDrive CI/CD for ML: automated eval gating, shadow deployments, canary releases, and rollback triggers /p /li /ul pTechnical Leadership /p ul li pSet architectural standards for AI systems across the group; conduct design reviews and own ADRs for major decisions /p /li /ul ul li pMentor ML engineers and applied scientists; grow the team's capabilities in production AI, not just prototype AI /p /li /ul ul li pCollaborate with Product and Commercial teams to translate business problems into ML problem formulations with clear success metrics /p /li /ul ul li pEngage with external research partners and track emerging work (arXiv, conference proceedings, open-source releases) to identify signals worth productionizing /p /li /ul h3Profile /h3 ul li p8+ years in ML Engineering, Applied AI, or Research Engineering with at least 2 years in a lead or staff-level role /p /li /ul ul li pDeep, hands‑on experience with LLMs in production: fine‑tuning, RLHF/DPO, prompt engineering, RAG, and tool use /p /li /ul ul li pFluent in Python and the core ML stack: PyTorch, Transformers (HuggingFace), PEFT/LoRA /p /li /ul ul li pReal experience with LLM inference serving — vLLM, TensorRT-LLM, or TGI — in a latency-sensitive production environment /p /li /ul ul li pPractical knowledge of agentic frameworks: multi-agent coordination, tool‑call orchestration, context/memory management, and observability (Langfuse, Opik, or equivalent) /p /li /ul ul li pExperience with speech AI (ASR/TTS pipelines) or real-time audio systems is a strong plus /p /li /ul ul li pSolid understanding of MLOps: experiment tracking (MLflow/WB), model registries, containerization (Docker/Kubernetes), and CI/CD for ML /p /li /ul ul li pAwareness of LLM-specific risk: hallucination, prompt injection, data leakage, fairness, and privacy — and how to mitigate them in production /p /li /ul ul li pStrong communication

skills:
you can write a crisp d