Top Artificial Intelligence Jobs in Milan

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

Build and maintain production-grade agentic AI systems for an internal AI platform. Design AWS infrastructure, knowledge graph solutions using Neptune or Neo4j, and LLM-based reasoning and retrieval workflows. Troubleshoot integrated systems, collaborate across teams, influence technical direction, and support engineering best practices. The contract is expected to last 4–6 months, with possible extension, and requires strong Python, AWS platform engineering, and agentic AI expertise.

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.

Build and deploy production-grade LLM capabilities for the Kayzen Console. Responsibilities include developing AI-backed APIs and full-stack features, implementing RAG, embeddings, agents, and tool calling, and establishing evaluation, monitoring, observability, and feedback systems. The role optimizes quality, latency, reliability, and cost; troubleshoots production issues; collaborates with Product, ML, and Engineering; and turns prototypes into maintainable AI products.

No responsibilities or duties were listed in the job description.

Leads global privacy and AI legal matters for a SaaS employment platform. Advises Product, Engineering, Security, and business teams on privacy by design, AI governance, data protection, workforce data, incidents, regulatory compliance, vendor reviews, transfers, and contract terms. Builds scalable policies, controls, templates, training, and customer-facing trust materials while monitoring global privacy and AI regulations.

Develop and deploy AI-powered capabilities across product and business teams. Act as a forward-deployed engineer, embedding with finance, sales, marketing, and product teams to introduce agentic methodologies, support AI adoption, shape the company platform and strategy, and translate learnings into scalable solutions. The role emphasizes production-quality code, business impact, adaptability, collaboration, and effective work in a global distributed environment.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design, build, and operate the AI storage layer for GPU-heavy edge and core deployments. Architect and optimize storage for distributed training and inference, integrate storage with Kubernetes (CSI), tune performance (RDMA, NVMe-oF, GPU Direct Storage), lead reliability and operational practices, and provide staff-level architectural ownership and cross-team influence while executing hands-on storage engineering.

Provide technical support for ClickHouse and Langfuse customers across EMEA and global regions through cases, chat, Slack, community channels, and calls. Troubleshoot database, cloud infrastructure, AI application, tracing, and evaluation issues within SLA and on-call rotations. Develop documentation and educational content, assist with technical proofs of concept, mentor colleagues, escalate bugs, collaborate with Engineering and Product teams, and support customer implementations directly in codebases.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

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

Participate in a 14-day remote pilot using a qualifying Google Pixel device to interact naturally with notification-generating apps, complete daily 10-15 minute notification sorting tasks, submit daily feedback and documentation, and report technical issues. Must follow instructions and maintain confidentiality via the Upwork platform.

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.

Design, build, and scale a global voice and automation AI platform: implement STT/TTS and telephony stacks, integrate and optimize LLM APIs and RAG pipelines, create production-grade LLM orchestration and enterprise automation workflows, and support international deployment and delivery.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.