Top Artificial Intelligence Jobs in Romania

Lead complex financial analysis, forecasting, and capital strategy for HPC and AI business. Advise leaders on strategic decisions, drive process improvements and GAAP adherence, represent business in cross-functional planning, and mentor finance analysts.

The Technical Architect will design and oversee modular AI-optimised software systems, ensuring scalability and resilience, while mentoring teams in AI-assisted coding practices and architectural governance.

Lead EMEA finance activities for HPC & AI business: deliver complex financial analyses, forecasting, and reporting; consult with business leaders on strategy; drive process and policy improvements; lead cross-regional finance initiatives; mentor analysts and ensure strong financial controls and GAAP compliance.

Lead short-term AI adoption program: build internal AI champions, create usage policies and training, support teams implementing AI tools, contribute to internal AI tool builds, and ensure responsible, secure deployment across distributed teams.

Lead architecture and hands-on development of an AI-native underwriting platform. Design scalable backend services in Python, integrate with React/TypeScript frontends, promote engineering standards, implement observability and security, and use AI-assisted development tools to improve productivity while mentoring the team and driving the platform from MVP to production.

Design, build, and maintain production-grade Generative AI helpdesk automation using N8N and cloud infrastructure. Implement workflow orchestration, CI/CD, infrastructure automation, observability, and troubleshoot end-to-end integrations with Jira, Slack, and other systems.

Lead end-to-end implementations of QVAC-powered decentralized AI solutions, coordinating clients, engineering, product, and legal. Translate business needs into deployment roadmaps, manage milestones and risks, ensure production readiness, and produce governance, documentation, and executive reporting across multiple concurrent projects.

Lead design, development and deployment of scalable AI/LLM systems (RAG, agentic systems). Architect end-to-end ML pipelines, apply MLOps/LLMOps, integrate AI with enterprise platforms, ensure security/privacy/compliance, mentor teams, and drive presales and delivery for enterprise clients.

Join the MLOps team to deploy, monitor and optimize machine learning models in production. Build data and model pipelines, implement CI/CD, ensure model performance and scalability, and collaborate with cross-functional teams to improve infrastructure and delivery.

Design, build and deploy autonomous AI agents and orchestration workflows to automate complex business processes. Lead PoCs with LLMs, integrate agents with enterprise systems, set governance and best practices, and upskill teams to adopt agentic automation.

Lead design and operation of ML infrastructure and pipelines, deploy and optimize scalable production models, integrate real-time and batch inference, mentor ML engineers, drive adoption of new tools, and communicate ML strategy with stakeholders.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

The Lead AI Engineer will design and implement enterprise AI platforms, focusing on integrations, automation workflows, and performance optimization, ensuring secure adoption across the organization.

Post-sales, customer-facing role that drives developer adoption of AI tooling by designing and running hackathons, Customer Developer Days, and repeatable facilitation infrastructure. Partner with deployment and success teams to diagnose adoption stalls, run high-touch interventions, capture outcomes, and evangelize results. Must be technically credible with developers and able to troubleshoot and demonstrate AI coding tools in real time.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Evaluate AI coding agents end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide opinionated feedback, distinguish response quality levels, and help define standards for great AI-developer interactions. Participate in take-home evaluation and interview as part of a rolling, project-based engagement.

As a Senior Data Engineer, you will architect scalable data solutions, develop ETL pipelines with cloud technologies, ensure compliance, and mentor junior engineers while enhancing cybersecurity capabilities.

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.

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

The Senior Data Engineer will design and maintain data pipelines, develop ETL processes, optimize GCP services, ensure data quality, and collaborate with stakeholders.

Post-sales technical lead for strategic EMEA accounts, advising on cloud-native and AI deployments. Own technical relationships, troubleshoot escalations, design architectures for LLM/agentic workloads, drive product adoption, deliver training, and feed customer insights to Product and 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.

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.

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.