Top Artificial Intelligence Jobs in Romania

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The AI Engineer will design and develop AI-powered features, evaluate AI/ML services, and integrate them into a cloud-based platform, working closely with cross-functional teams.

Develop new algorithms for Motor Control applications, validate algorithms, collaborate with system engineers, and maintain software products following quality standards.

Evaluate personalized AI responses by designing multi-turn prompts using your own personal context, assessing grounding, integration, and helpfulness, stack-ranking side-by-side model outputs, writing clear rationales, verifying debug info, and maintaining data hygiene. Work full-time as a remote contractor with schedule overlap requirements.

Evaluate personalized AI responses for Gemini by designing multi-turn prompts using personal data, comparing side-by-side responses, assessing grounding, integration, and helpfulness, writing structured rationales, extracting debug info, and maintaining data hygiene.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

As an AI-First Frontend Engineer, you will develop high-performance, AI-integrated user interfaces for telehealth applications, manage team workflows, and optimize web performance.

The Full-Stack Developer will enhance an AI-driven platform by developing features in both frontend and backend, focusing on QA chatbot, evaluating modules, and scalability of processing workflows.

The Industry AI Consultant Engineer will design and implement AI solutions, lead workshops to define use cases, and collaborate with cross-functional teams to deliver AI products.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The Full-Stack Engineer will design, develop, and maintain web applications, engage in innovative projects, and communicate with AI teams, working in an Agile environment.

The AI & Automation Intern will assist in creating internal tools, AI workflows, and automations, while collaborating with management to enhance operations. Responsibilities include researching AI technologies, building applications, and documenting processes.

Define and deliver AI-driven features across HCM, TSCM and voyage compliance product lines. Identify AI/ML opportunities, translate models into product requirements, run discovery with customers, measure outcomes, ensure responsible AI practices, and partner with cross-functional teams to launch and commercialise solutions.

Own the AI platform roadmap and build reusable capabilities (prompting, RAG, tool calling, agents, evaluation, observability). Run discovery, prioritize AI investments, partner with Product, Engineering, Design and Data, guide other PMs, define success metrics, and ensure scalable, reliable AI-powered features that deliver customer and business value.

Design, build, and maintain Python-based backend services and production-grade LLM applications. Implement end-to-end RAG, vector search, multi-agent systems, secure MCP servers, event-driven architectures, and monitoring. Improve prompts, deploy at scale, and collaborate across teams to optimize performance, reliability, and security.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Design, build, and operate cloud-native full-stack web applications for healthcare SaaS. Integrate LLMs and agentic AI features (retrieval, tool use, workflows). Own reliability, security, DevOps, and collaborate with product, UX and architects to deliver AI-driven clinician and patient value.

Design, develop, and maintain AI and generative models (LLMs); build and support reliable, performant AI systems; craft and optimize prompts; collaborate with cross-functional teams from scoping through release; contribute to engineering and product discussions and experimentation.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

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.

Manage end-to-end release lifecycle for a multi-agent AI platform: own release calendar, plan and orchestrate feature/patch/hotfix deployments, coordinate cross-functional teams, enforce quality gates and compliance, monitor release performance, run post-release reviews, mitigate risks, and lead release communications and escalations.

Lead QA for AI platforms by defining test strategy, automation, and quality governance. Design AI-specific and CI/CD-integrated test frameworks, validate agent behavior, manage QA team, enforce quality gates, drive metrics and continuous improvement across engineering and product teams.