Top Artificial Intelligence Jobs in Romania

Provide second-level operations support for core and telco cloud infrastructure: manage incidents, preventive maintenance, ticket lifecycle, performance and lifecycle management, automation scripting, procedures, team coordination and training. Role includes project support, reporting insights, and a six-month deployment to Saudi Arabia.

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.

Open unsolicited applications for IT professionals across software development, DevOps, cloud, data migration, ECM, QA, AI, data analysis, project management, and IT operations. Join the talent pool to be considered for future roles; strong communication, technical background, problem-solving, teamwork, and growth mindset expected.

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.

Lead KPI analysis to drive business performance, identify gaps and implement improvements, support strategic decisions with data insights, work with leadership across locations, propose digital and AI solutions, and travel to commercial sites to implement initiatives.

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 end-to-end product backlog for procurement interoperability and orchestration across source-to-pay. Design integration and data alignment across procurement platforms, run stakeholder workshops, translate demand into user stories and releases, manage E2E testing, rollout and BAU transition, and embed AI-driven automation and prompt-driven workflows to improve efficiency.

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.

Design and build production-grade AI applications and services in Python, integrating LLMs with backend APIs and enterprise systems, implementing evaluation frameworks, observability, CI/CD, and reusable components to ensure reliable, testable, and scalable AI features.

Lead design and governance of ICON's end-to-end AI platform, multi-agent frameworks, and supporting services. Define cloud-native, scalable architectures, integration patterns, APIs, and standards; lead solution architecture, design reviews, and mentor engineering teams to deliver secure, high-performing AI/ML production systems.

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.

Design, build and deploy AI agents and end-to-end solutions using Microsoft Copilot Studio, Microsoft 365 Copilot, and Azure AI. Work with DACH customers to identify AI opportunities, translate business requirements into technical solutions, ensure security and governance, and deliver scalable automation that creates measurable business value.

Design, build, and own AI/LLM-assisted product features in Ruby on Rails: integrate LLM APIs, combine AI with business logic, tests, fallbacks and safeguards, improve reliability/latency/cost/security, write automated tests, and debug production issues.

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.

The BI Engineer will create and maintain dashboards, support analysis, ensure data integrity, and contribute to data strategy initiatives.

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.

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.

Design, develop, and deploy ML solutions for localization workflows (machine translation, LLM fine-tuning, QA). Own projects end-to-end, implement and optimize models using Python and ML frameworks, deploy via Docker and AWS (SageMaker/EC2/S3), monitor production, document experiments, and collaborate across teams.

Design, develop, and deploy production-grade AI services and microservices for biomedical data platforms. Implement LLM integrations (RAG, embeddings), build ETL/data pipelines, enhance NLP extraction workflows, train and evaluate ML models, and ensure observability, testing, and reliability of AI systems supporting genomics and life-science research.

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.

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.

Produce high-quality AI-generated visual assets (photorealistic humans, lifestyle, product imagery) for performance marketing. Iterate on creatives using data, collaborate with marketers/copywriters/video editors, integrate AI outputs into design pipelines, ensure brand consistency and production readiness, and document work in ClickUp.

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.