Top Artificial Intelligence Jobs in Italy

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

Work with clients and Customer Success to design, build, and maintain server-side components for an AI platform. Implement and optimize databases, RESTful APIs, security, distributed systems, and performance; troubleshoot bugs; document code and collaborate with front-end engineers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Develop a Python-based AI/data pipeline to parse EDA outputs and PDFs, combine NLP, computer vision, LLM/LVM and graph methods to map circuit topologies, integrate historical failure data, predict failure modes, generate standardized FMEA reports, and validate the proof-of-concept with hardware engineers on a baseline board.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Build and maintain Kong AI Gateway features (multi-LLM routing, semantic caching, MCP), implement proxying, load balancing, database support, troubleshoot bugs, optimize performance, and integrate with third-party deployment platforms.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Build and operate the internal AI platform used by 600+ developers: secure AI coding environments, AI-assisted CI and agentic AWS workflows. Own products end-to-end, improve developer experience, ensure reliability/observability/cost-awareness, participate in on-call, and capture reusable patterns and templates for broad adoption.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Design, build, test, and deploy AI-powered applications for scientific and healthcare workflows. Develop data pipelines, integrate LLMs and RAG systems, write Python code, create evaluation benchmarks, validate AI outputs, and productionize scalable, tested solutions while mentoring teammates and guiding technical decisions.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Own vision, strategy, and roadmap for AI and platform capabilities in a cloud-native data protection product. Define AI-driven features (anomaly detection, classification, risk scoring), platform services (APIs, data pipelines, extensibility), and end-to-end user experiences (policies, RBAC, onboarding, reporting). Partner with engineering, design, security, and go-to-market to prioritize, launch, and iterate on enterprise-grade, explainable AI features and measure success with data-driven metrics.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Support technical and content SEO for Doctolib Italy: perform crawlability and Core Web Vitals audits, keyword research, on-page optimization, competitive analysis, report on organic performance, and leverage generative AI to automate and improve SEO workflows.

Onboard and support client projects onto Nebius' Azure AI platform using runbooks and golden paths. Adapt Terraform modules and CI/CD pipelines, support ML workload deployment and lifecycle management, capture delivery gaps, and provide structured feedback to Platform Engineering while maintaining security and governance.

Own end-to-end creation of HTML5 playable ads for PeopleFun games. Build scalable tooling and processes, leverage AI to speed development, optimize UX/performance and cross-platform compatibility, and collaborate with marketing, producers, artists and animators.

Lead and build the AI foundation for an AI-native vulnerability management platform. Own AI strategy, evaluation frameworks for non-deterministic agents, fine-tuning and model-routing experiments, prototype and ship agentic features, and lead a small hands-on AI team to productionize breakthroughs in generative AI for cybersecurity.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Work with engineers and data scientists to develop, test, and integrate AI applications using Python and React. Implement and manage Azure cloud services, prepare and preprocess data for ML models, maintain documentation, and contribute ideas in a collaborative, growth-oriented environment.

Lead developer engagement across EMEA AI-native ecosystems to drive adoption of NVIDIA technologies. Act as technical advisor, enable partners with sample code and demos, onboard startups, map ecosystem opportunities, and collaborate cross-functionally to influence product roadmaps and scale developer programs.

The role involves designing AI-based financial tools, supporting consulting projects, creating digital learning content, and enhancing advisory activities.

Define, implement, and operate AI governance frameworks to ensure safe, ethical, and compliant AI use. Advise and coach teams, influence senior stakeholders, collaborate with legal, risk, privacy and security, and design lifecycle controls, risk assessments, approvals, audits, and monitoring. Translate technical AI concepts into policy, drive continuous improvement, and support enterprise AI standards and assurance.