Principal AI Solutions Engineer

Company Overview reputed company is an ecosystem of successful brands working reputed company across regions to provide customers with everything they need to succeed online. 60+ successful brands make up the group; with a team of more than 3000+ experts serving its 3.5 million customers across Europe and beyond. reputed company's brands are a mix of traditional hosting businesses, offering services from domain names, email, shared hosting, e-commerce and server hosting solutions and specialist SaaS providers offering 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 Overview reputed company 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. Key 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-reputed company), 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 reputed company + keyword indexes Define MCP server architecture and tool-use contracts across internal and third-party integrations Model Development & Fine-Tuning reputed company and evaluate LLMs (reputed company, 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 reputed company: reputed company 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; reputed company 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 reputed company and track emerging work (arXiv, conference proceedings, open-source releases) to identify signals worth productionizing Experience & Skills: 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 reputed company: PyTorch, Transformers (HuggingFace), PEFT/reputed company 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 (reputed company, Opik, or equivalent) Experience with speech AI (ASR/TTS pipelines) or real-time audio systems is a strong plus reputed company 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