Principal AI Solutions Engineer

Solutions EngineerSolutions EngineerFull TimeRemoteLeadTeam 1,001-5,000H1B No SponsorCompany SiteLinkedIn

Location

Belgium

Posted

15 days ago

Salary

0

Seniority

Lead

Postgraduate Degree8 yrs expEnglishDockerKubernetesPythonPyTorch

Job Description

Principal AI Solutions Engineer

team.blue

• Architect and evolve our multi-agent orchestration platform (currently built on Hermes / Multica) • 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 • Fine-tune and evaluate LLMs (LoRA, QLoRA, DPO) for domain-specific tasks including customer support, classification, and structured extraction • 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 • 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 • 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

Job Requirements

  • 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.

Benefits

  • Health insurance
  • 401(k) matching
  • Flexible working hours
  • Paid time off
  • Remote work options

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