Top Artificial Intelligence Jobs in Italy

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

The Senior Project Manager will lead complex corporate transformation programs using AI tools, manage financials, and engage senior stakeholders while ensuring delivery quality and mentoring junior team members.

Design scalable, secure architectures and reference blueprints for Generative AI, ML, and agentic workflows. Evaluate technical feasibility via POCs, define production requirements, optimize for cost, latency, and reliability, ensure AI governance/compliance, conduct model validation, and provide technical leadership and mentorship to align business, engineering, security, and infrastructure.

Design scalable, secure enterprise AI architectures and end-to-end pipelines (Generative AI, ML, agentic workflows). Drive technical feasibility, prototypes/POCs, model evaluation, cost/latency optimization, governance alignment, and mentor engineering teams to deliver production-grade AI solutions.

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Design and implement AI solutions, collaborate with clients to understand requirements, lead development of machine learning models, and support presales activities.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

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.

As a Tier 3 Support Engineer, you will resolve complex technical issues, work with engineering teams to enhance products, and document solutions.

Build and improve Restream’s web interfaces and user-facing features for the AI Clips team. Design UI and business logic, collaborate with product designers, participate in code reviews, improve system reliability, and evaluate emerging technologies. The role requires strong JavaScript and TypeScript expertise, advanced React knowledge, CSS preprocessor experience, and familiarity with backend development, cloud infrastructure, containers, and continuous deployment.

Lead design and ownership of shared libraries, Spring Boot starters, and agentic frameworks. Set standards for agent orchestration, evaluations, and guardrails. Mentor teams on Python-based agents (LangChain/LangGraph), drive adoption, prototype research directions, and improve developer experience across Kotlin backend and client libraries.

As a Customer Support Expert, you'll assist brands by offering customer support via social media and email, responding to inquiries, and ensuring positive customer experiences.

No responsibilities or duties were listed in the job description.

Build and ship production AI systems powering classification, taxonomy, PII detection, ROI estimation, and enterprise insights. Responsibilities include developing data and enrichment pipelines, improving LLM inference efficiency, extending MCP tooling, creating automated model evaluation frameworks, debugging customer-facing model issues, running AI product prototypes, and collaborating with Product and Engineering to deliver reliable, scalable AI features.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.