Top Artificial Intelligence Jobs in Hungary

The role involves designing authentication flows for AI agents, building secure token systems, and creating high-performance server-side components while collaborating with frontend teams and improving system performance.

Design, build, and operate scalable GCP cloud infrastructure for AI/ML platforms using Terraform and IaC. Manage multi-project environments, security, governance, cost control, hybrid networking, GPU provisioning, and self-hosted autoscaling CI/CD runners. Integrate infrastructure tooling into CI/CD pipelines and take end-to-end ownership of delivered solutions while collaborating with stakeholders.

Drive HR data quality and AI readiness by identifying root causes, implementing remediation, building automated validation and anomaly detection, partnering with Workday and process owners, and ensuring global HR data governance and standards for reporting, payroll, and AI/ML inputs.

Review, evaluate, and annotate AI-generated text, images, audio, and video for quality and guideline compliance. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve annotation guidelines and datasets for generative AI models.

Build and operate a composable, event-driven internal platform: design and ship backend services (Python/Go), frontend admin tools (TypeScript/React), integrations with SaaS and cloud APIs, BPMN/workflow orchestration, and LLM/AI tooling across the development lifecycle while owning production reliability, security, and observability.

Build and operate internal, event-driven platform services end-to-end: backend services (Python/Go) on GCP Cloud Run, TypeScript/React UIs, integrations with third-party SaaS (REST, webhooks, OAuth), BPMN workflow orchestration, and apply AI tooling across the development lifecycle while ensuring security, observability, and production reliability.

Build and operate internal, event-driven platform services and UIs across the stack. Integrate third-party SaaS via APIs/webhooks/OAuth, implement observable BPMN workflow orchestration, use AI tooling throughout the SDLC, review AI-generated code, and ensure production reliability, security, and cost control.

Build and operate internal, event-driven platform services and UI features end-to-end. Integrate third-party SaaS and cloud APIs, implement observable BPMN workflow orchestration, use AI tooling across the SDLC, ensure security and reliability in production, and contribute engineering standards and playbooks.

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

As a Senior AI Engineer, you'll architect and deploy AI solutions on GCP, transform AI experiments to production, and conduct training sessions.

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 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.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead AI adoption for business teams: run assessments, prioritize use cases, design and deliver workshops, activate champions, remove blockers, track adoption KPIs, and hand teams off as self-sufficient while routing engineering needs to technical teams.

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.

The Senior AI Engineer will design and implement core AI systems for the Compliance in a Box platform, collaborating with cross-functional teams and ensuring performance and quality of AI agents.

The AI Software Developer will create AI-driven applications, collaborate with teams, analyze requirements, and design scalable products to solve real-world problems.

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.

The Head of Performance will lead performance marketing strategies, focusing on optimizing paid acquisition channels and utilizing AI tools for data-driven decisions.

Lead and build the AI foundation for an AI-native vulnerability management platform. Own AI strategy, evaluation frameworks for non-deterministic agents, fine-tuning and model-routing experiments, prototype and ship agentic features, and lead a small hands-on AI team to productionize breakthroughs in generative AI for cybersecurity.

Design, build, and operate production-grade automations that reduce manual work across Operations, Support, Finance, and Tech. Identify high-volume automation opportunities, integrate with existing systems via APIs/webhooks, apply AI/LLMs for classification/routing/summarization, and ensure reliability, observability, and iterative improvement of workflows.

The Software Engineer will develop AI applications for customer experience using full-stack skills, handling features end-to-end, and collaborating with various stakeholders.

Design, build, and optimize AI-driven automation workflows using LLMs, no-code/low-code platforms and custom APIs. Integrate CRMs and internal systems, develop Python/JavaScript backend logic, monitor performance, and collaborate with stakeholders to scale automated processes.

Design, develop, and deploy AI-driven applications by integrating ML models and APIs into scalable backend systems. Collaborate with cross-functional teams, contribute to architecture, build CI/CD and containerized environments, use cloud platforms, ensure code quality, monitor production AI features, and stay current on ML frameworks.