Top Artificial Intelligence Jobs in Milan

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.

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.

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.

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.

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 Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

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

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Review, evaluate, and annotate AI-generated text, images, audio, and video for quality, consistency, and guideline compliance. Identify edge cases, perform calibration activities, provide feedback to improve annotation workflows, and contribute to high-quality multilingual datasets for training generative AI models.

The role involves evaluating AI-generated outputs, analyzing multimedia content, providing feedback, and ensuring culturally relevant content based on local insights. Attention to detail and analytical skills are essential.

Senior individual contributor who architects, prototypes, and productionizes agentic AI and ML forecasting solutions for Finance. Designs multi-agent systems, ML models for forecasting and anomaly detection, establishes governance/responsible AI practices, and partners with FP&A, Engineering, IT, and Security to deliver measurable automation and forecasting impact.

Design and implement CI/CD and IaC for LLMs, automate model and dataset versioning, provision GPU/TPU infrastructure, enable safe experiment gates and progressive delivery, and build monitoring/observability and feedback loops for production LLM endpoints.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.