Senior GenAI Engineer

2 mesi fa

Candidati

Torino, Torino, Italia The Italian Institute of Artificial Intelligence Tempo pieno

In short

Scope: Tech Lead / Forward Deployed Engineer for LLM + agentic architecture, infrastructure, and deployment
Salary range: €70,000 – €90,000 gross/year (depending on experience)
Location: Turin (Hybrid)
Stack: Python, LLM, Agent Orchestration, RAG, Vector DBs, MCP servers, CI/CD, Observability, Docker, Kubernetes, Azure/AWS, Edge/On‑prem

About AI4I And Our Culture

The Italian Institute of Artificial Intelligence (AI4I) is headquartered in the standout location of OGR, Turin, with the unique mission to support both applied research and on-site deployment of AI. We are established to generate real-world impact at the intersection of science, technology, and industrial transformation.

Research units focus on topics such as secure and trustworthy AI, physical AI and multi-agent systems while the deployment unit is responsible for the implementation at client site. A dedicated high-performance computing center, including next-generation GPU systems such as NVIDIA B200 accelerators, is available to support both research and industrial deployment.

Our System User Knowledge platform and OGR-based network connect corporates, SMEs, startups, and scientific partners, fostering workshops and collaboration with external partners to remain at the forefront of innovation.

Role Summary

Within the Deployment Unit, we are hiring a Senior GenAI Engineer who will operate as a hands-on tech lead for client projects, owning end-to-end architecture, infrastructure, and production deployment of LLM applications. This is a highly hands-on role for someone who combines strong software engineering skills with deep knowledge of the GenAI ecosystem, including RAG architectures, orchestration frameworks, evaluation methods, and deployment patterns.

You Will Work Closely With

Deployment and Data Science team, delivering production-grade AI services
Research units, to industrialize promising use cases emerging from applied research
Industrial and institutional partners, ensuring solutions meet operational, safety, and compliance requirements

What You Will Do

Lead AI delivery (solution architecture, infrastructure decision and deployment) in projects and scope them and their technical feasibility through client interaction.
Configure foundation models and define prompting + tool-use strategies for business workflows.
Evaluate and select the most appropriate AI providers and frameworks based on performance, cost, latency, safety and scalability.
Define and implement approaches for prompt evaluation, hallucination mitigation, guardrails, observability, and human-in-the-loop review where needed.
Deploy and operate GenAI services in production integrating LLM APIs, vector databases, orchestration frameworks, backend services, cloud infrastructure, and monitoring.
Apply agentic AI-assisted coding to reduce cycle time and improve results across the above responsibilities.

About You

Master’s degree in Computer Science, Engineering, Physics or related; PhD a plus.
Strong hands-on experience (5+ years) building and deploying applications based on deep learning frameworks (e.g. TensorFlow, PyTorch), RAG architectures, task-specific copilots or multi-step reasoning systems.
Strong Python skills and solid experience with agentic AI‑assisted coding (e.g., Claude Code, Codex), modern LLM tooling (e.g., LangChain, LangGraph, ADK) and API integration patterns.
Solid data foundations: SQL and experience with data warehouses and ETL/ELT pipelines; familiarity with vector databases (e.g., pgvector, Qdrant, Redis).
Experience with evaluation frameworks: model choice, prompt strategies, retrieval tuning with attention to latency and token/cost usage.
Production-grade delivery: Kubernetes + Docker + Git and hands-on exposure to CI/CD, observability and tracing tools such as OpenTelemetry, Grafana/Prometheus.
Hands-on experience in designing cloud enterprise solutions for GenAI workloads (e.g., Azure OpenAI/ Bedrock/ Vertex AI) with at least one hyperscaler stack (Azure/ AWS/ etc.), including deployment, monitoring and cost control.
Familiarity with multiple AI providers and model ecosystems, including commercial APIs and/or open-source models.
Proficiency in Italian and Engli

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