Top Artificial Intelligence Jobs in Rome

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

The Chief Data & AI Officer will lead the establishment of a company's data and AI infrastructure, ensuring reliable information capture, decision-making processes, AI system integration, and compliance with governance standards.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Remote role for ML/AI experts to assess AI-generated content, craft and answer advanced technical prompts, evaluate model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Design and deliver Salesforce solutions focused on AI and automation. Translate business needs into architectures and integrations, develop AI use cases (Agentforce, Einstein, Copilot), support project teams, mentor juniors, and manage client stakeholders.

Lead AI search strategy to secure brand authority in LLM-driven results. Design and run tests to measure LLM visibility, build analytical frameworks, integrate data sources, collaborate with Product/Engineering to optimize ingestion, and drive PR/community strategies to shape AI perception.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

Support sales by delivering technical demos, POCs, workshops and architecture guidance. Translate customer requirements into solutions, enable partners, respond to procurement requests, and coordinate with product and engineering throughout the pre-sales lifecycle.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

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.

The Developer Relations role involves engaging and educating the developer community, creating technical content, and advocating for the company's technology while collaborating with cross-functional teams to enhance developer experience.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

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.

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.

Lead short-term AI adoption program: build internal AI champions, create usage policies and training, support teams implementing AI tools, contribute to internal AI tool builds, and ensure responsible, secure deployment across distributed teams.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

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.

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.