Top Artificial Intelligence Jobs in Italy

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Lead strategic AI adoption for enterprise CX: discover use cases, design reference architectures, deliver rapid POCs/MVPs, integrate AI with CRM/ERP, build evaluation pipelines, and mentor teams to scale responsible, outcome-oriented agentic AI solutions.

Design, implement, and operationalize cloud and AI security architectures. Build cloud secure foundations, integrate enterprise security platforms, develop AI-powered security capabilities, automate compliance, run security assessments, and support DevSecOps practices and delivery teams.

Design and implement secure cloud and GenAI architectures, engineer cloud security controls, integrate enterprise security platforms, build AI-enabled security capabilities, conduct security assessments, and operationalize DevSecOps and IaC practices across client environments.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

As a Senior Machine Learning Engineer, you will develop and scale customer-facing AI models, ensuring quality integration with business solutions, and mentoring peers.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

As an AI Senior Business Consultant, you will leverage AI technologies to drive strategic business opportunities, analyzing trends, and facilitating AI-driven transformations across multiple industries.

Lead end-to-end implementation of QVAC-powered AI deployments, acting as primary client contact, coordinating cross-functional teams, defining roadmaps, managing risks, and ensuring production rollouts while aligning product capabilities with client objectives and documenting reusable architectures and lessons learned.

Design and implement AI solutions using ML models and NLP, collaborate with teams, and support business process improvement through AI.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Design, build and deploy AI solutions (custom copilots, conversational agents, embedded AI) using Microsoft Copilot Studio and Azure AI Foundry. Integrate AI with Power Automate, Snowflake and enterprise systems, collaborate with stakeholders, operationalise models, monitor performance, and champion responsible AI practices.

The role involves designing AI solutions, developing machine learning models, collaborating with clients, and participating in presales activities.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Design, build, and ship production-grade LLM-powered features (RAG, extraction, summarisation, agentic tooling). Build data and inference pipelines, integrate with back-ends and vector stores, ensure evaluation, monitoring, cost and performance optimisations, and champion testing and reliability alongside product and tech leads.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.