Principal AI Solutions Engineer

Role Overview

• 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

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.

Tech Stack

• Docker
• Kubernetes
• Python
• PyTorch

Benefits

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