Senior AI/ML Engineer
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As Principal AI Solutions Engineer, you own end-to-end AI systems for a large European digital-services ecosystem. You set technical direction, ship production AI, and raise the bar for a fast-moving team. You work across model research, fine-tuning, agent-based orchestration, and real-time inference to power hosting, domains, email, and SaaS for millions of SMBs. You will shape architecture, guardrails, and reliable delivery with cross-functional partners.
Responsabilità- Architect and evolve multi-agent orchestration platform (Hermes / Multica) with plugin systems, tool pipelines, observability, and channel adapters
- Design and implement voice AI pipelines with low latency (STT, real-time TTS, VAD, SIP/RTP)
- Build and maintain RAG pipelines with retrieval quality measurement and hybrid search
- Define MCP server architecture and tool-use contracts across internal and third-party integrations
- Fine-tune and evaluate LLMs for domain tasks (customer support, classification, extraction)
- Evaluate model quality using automated and human metrics; manage model lifecycle from experiments to production
- Own AI observability stack (tracing, instrumentation, cost tracking, quality alerts) and enforce guardrails
- Develop data ingestion/preprocessing and feature pipelines; drive ML CI/CD, shadow deployments, canary releases
- Set architectural standards for AI systems; conduct design reviews; mentor engineers; collaborate with Product/Commercial
- Engage with external research partners to identify signals for productionization
- 8+ years in ML Engineering, Applied AI, or Research Engineering with leadership experience
- Deep hands-on experience with production LLMs: fine-tuning, RLHF/DPO, prompt engineering, RAG
- Proficiency in Python and core ML stack: PyTorch, Transformers (HuggingFace), PEFT/LoRA
- Experience with LLM inference serving in latency-sensitive environments (vLLM, TensorRT-LLM, or TGI)
- Knowledge of agentic frameworks: multi-agent coordination, tool-use orchestration, observability
- Experience with speech AI or real-time audio systems is a plus
- Strong MLOps understanding: MLflow/W&B, model registries, Docker/Kubernetes, ML CI/CD
- Awareness of LLM risk management (hallucination, prompt injection, data leakage, privacy) and mitigation
- Excellent communication: ability to write design docs, run architecture reviews, and explain tradeoffs
- strong communication
- leadership and mentorship
- collaboration with cross-functional teams
- Hermes / Multica orchestration
- voice AI pipelines (ASR, TTS, VAD, SIP/RTP)
- RAG and hybrid search
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