Top Artificial Intelligence Jobs in Romania

The role involves designing and deploying AI systems for clinical operations, developing models for decision-making, and ensuring AI implementation aligns with data governance standards. Responsibilities include collaborating across teams, enhancing operational efficiency, and advancing AI applications in a regulated environment.

Lead design and deployment of AI agents and workflow automations across Supply & Operations, coach and lead Operational Excellence (Lean/Six Sigma) projects, build OE capabilities, embed continuous improvement and AI-driven workflows into enterprise systems, and lead global OE programs and strategies in External Supply.

Lead global payroll strategy and transformation for 30,000 employees across 56 countries, driving AI/RPA and middleware integrations, ensuring SOX and global data/tax compliance, consolidating vendor ecosystem, aligning payroll with Total Rewards and Finance, and upskilling the global payroll delivery team.

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

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.

Lead management and optimization of Crayon and Clozd platforms, integrate them with CRM and sales enablement systems, drive cross-functional adoption, execute win-loss programs, monitor platform metrics, and deliver actionable competitive and buyer insights to improve win rates and product roadmaps.

The CSM Transformation PMO Lead will manage the transformation roadmap, oversee governance across initiatives, and partner with stakeholders to ensure successful change implementation.

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.

Sell Libra AI to Romanian small/mid-size law firms and in-house legal teams. Manage full sales cycle from inbound/outbound prospecting to demo, negotiation, and close. Collaborate with Legal Engineering, Customer Success, and Marketing to tailor demos, onboard customers, maintain pipeline, and meet ARR targets using data-driven outreach and sales methodologies.

Build production-grade AI and automation systems to integrate ad platforms, CRM and analytics for Performance Marketing. Ensure data accuracy by reviewing SQL and data models, create monitoring/diagnostics and human-in-the-loop decision workflows, and roll the solution out across nine markets.

The Engagement Manager leads AI implementation initiatives, engaging with clients, coordinating technical delivery, managing stakeholders, and overseeing project execution from initiation to production deployment.

The role involves editing and producing narrative-driven video content using AI tools, managing entire production workflows, and ensuring visual coherence and storytelling excellence.

Lead the development of AI agents for data analysts, focusing on automation, governance, and performance evaluation while collaborating with stakeholders.

Design and implement Generative AI and Agentic AI solutions, integrating AI models with real-world applications, and managing AI workloads in cloud environments.

Lead the design, implementation, and deployment of Generative and Agentic AI solutions, integrate LLMs and databases, and manage cloud infrastructure.

The AI Full-Stack Developer will build AI-enabled web applications using React and NestJS, integrate LLM APIs, deploy on AWS, and work in an Agile environment with cross-functional teams.

Lead sales pursuits for private cloud AI solutions, build and manage pipeline, collaborate with account managers, develop pursuit plans, engage clients up to C-level, contribute to proposals and negotiations, drive renewals, and use Siebel for forecasting and deal management. Requires vertical industry knowledge and regional language skills.

Define technical vision and roadmap for the Ask.ai platform, select models and infrastructure, set standards and governance, coordinate AI engineers and contributors, design integrations and platform architecture, ensure security and data privacy, and provide technical mentorship and reusable components to accelerate AI solutions across teams.

Design, prototype, and productionize enterprise-grade generative and agentic AI solutions. Architect multi-agent systems, integrations (ERP/APIs), observability, security, and governance. Build reusable CoE assets and ensure deployment, monitoring, and operational readiness.

Build and maintain AI-powered internal tools and agentic workflows for operations. Ship rapid iterations (multiple daily releases), integrate third-party APIs, own QA, and translate ops feedback into production deployments.

The Senior Python AI Software Engineer at Nearform designs and builds applications, optimizes performance, collaborates with clients, and supports various projects, focusing on Python and AI solutions.

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.

Design, build, and optimize high-performance AI infrastructure systems to support intelligent agents, ensuring reliability and scalability for millions of users.

Independently validate and test generative AI models across business use cases: design SME question sets, perform hands-on testing, identify and escalate risks, document outcomes, challenge development teams on methodology, collaborate with model owners, and mentor junior validation staff to ensure robust, fair, and fit-for-purpose models.

Hands-on platform engineering role building and operating an internal Agentic AI platform: implement infrastructure-as-code, security and secrets management, GitOps delivery, observability, autoscaling, API/AI gateway routing, and run day-to-day operations including incident response, backups, and runbooks.