Top Artificial Intelligence Jobs in Milan

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

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.

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.

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.

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.

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.

Assess client engineering workflows, design and build agentic tooling and processes, instrument solutions for cost and business outcomes, run demos and enablement sessions, support teams to independent operation, and contribute technical leadership to presales when needed.

Evaluate Italian AI-generated responses for accuracy, relevance, and guideline adherence. Identify errors, provide rationale, assess annotations, attend calibration sessions, and maintain high-quality consistent judgments.

Lead analysis and improvement of conversational AI systems by designing prompt strategies, optimizing RAG and retrieval, evaluating LLMs, defining AI quality metrics, running experiments, and collaborating with engineers to deploy production-scale improvements.

Design, build, and productionize AI features including conversational and voice AI. Integrate LLMs and AI APIs, implement RAG and agent workflows, troubleshoot production issues, optimize performance and costs, and collaborate with product and AI science teams to deliver scalable, reliable AI systems.

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.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Design and build Python-based AI applications, optimise performance and storage, implement CI/CD and testing, collaborate with clients and distributed teams, and support technical leads to deliver scalable, production-grade solutions.

As an AI Engineer at Lexroom, you will build full-stack features using Next.js and Python, optimize APIs and databases, and collaborate with teams to enhance legal software products.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

This program involves developing and deploying AI models, collaborating with teams, and working on high-impact products in a structured training program.