Top Artificial Intelligence Jobs in Romania

Lead international commercial build for data, engineering and AI: own data foundation, shared engineering pool, solution architecture, AgentOps, AI evaluation, model routing and CIO platform integration to deliver production-grade, governed AI and data products.

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.

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

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

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 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.

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.

Maintain and enhance SE Power BI dashboards, validate data integrity, support Salesforce (including Vivun) triage and basic admin, prepare data for quota planning, document processes, and apply AI-assisted tools to streamline reporting and operational workflows.

Play modern AAA games to capture, record, and annotate structured gameplay data per instructions. Complete specific in-game tasks, troubleshoot recording issues, organize and securely transfer files, and report anomalies. Flexible freelance contractor role, fully remote.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

Design and deploy production-grade AI solutions to improve automotive inventory discoverability, generate scalable vehicle content, cluster search intent, optimize ranking signals, identify high-potential inventory combinations, measure impact via experimentation, and collaborate with product and engineering teams to operationalize SEO and GEO initiatives.

Develop and maintain scalable backend systems and REST APIs in C#/.NET, optimize SQL Server databases, troubleshoot production issues, and use AI-assisted development tools to accelerate delivery while maintaining code quality.

Lead cloud and AI architecture: define scalable, secure system designs, technology stacks, and workflows; guide DevOps and engineering teams; promote AI/ML best practices; mentor developers and ensure delivery alignment with business goals.

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.

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.

Own design, deployment, and operation of high-performance AI GPU fabrics and datacenter networking (RoCE, RDMA, BGP/ECMP, EVPN/VXLAN). Provide staff-level architecture, cross-team technical leadership, incident escalation, automation (Python/Bash), and operational standards for global datacenter and edge deployments, including inter-datacenter transport and security.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Lead the design and delivery of AI-enabled full-stack applications, ensuring solutions meet enterprise standards while mentoring other engineers.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.