Senior Ai/Ml Engineer

Team.Blue; Toscana; In loco; EUR 150.000 - 190.000; Tempo pieno

Descrizione del lavoro

Team.Blue in Toscana, Italy seeks a Principal AI Solutions Engineer to own end-to-end AI systems, set technical direction, and ship production AI across a fast-moving team. You will lead across model research, fine-tuning, agent-based orchestration, and real-time inference for hosting, domains, email, and SaaS for millions of SMBs.

Responsibilities include evolving multi-agent orchestration platforms, building low-latency voice AI pipelines, and developing RAG pipelines with rigorous quality

Competenze

• 8+ years in ML Engineering, with leadership experience.
• Hands-on production LLMs experience: fine-tuning, RLHF/DPO, prompt engineering.
• Proficient in Python and core ML stack (PyTorch, Transformers, PEFT/LoRA).
• Experience with latency-sensitive LLM inference (vLLM, TensorRT-LLM, or TGI).
• Knowledge of multi-agent orchestration, tool-use, and observability.
• Awareness of LLM risk management (privacy, data leakage, hallucination).

Mansioni

• Architect and evolve multi-agent orchestration platform (Hermes / Multica).
• Design and implement voice AI pipelines with low latency.
• Build and maintain RAG pipelines with retrieval quality measurement and hybrid search.
• Define MCP server architecture and tool-use contracts across integrations.
• Fine-tune and evaluate LLMs for domain tasks and production lifecycle.
• Own AI observability stack (tracing, instrumentation, cost tracking).
• Drive ML CI/CD, shadow deployments, canary releases; mentor engineers.

Conoscenze

Leadership experience

Python proficiency

LLM fine-tuning

RAG & hybrid search

Observability & tooling

Communication skills

Mentorship

Strumenti

PyTorch

Transformers

PEFT/LoRA

Descrizione del lavoro

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.

• 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
• 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
• RAG and hybrid search