Top Artificial Intelligence Jobs in Hungary

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.

Lead full life-cycle development of cloud-native SaaS TMS solutions: prototype, design, implement front-end (React) and back-end (.NET/C#) systems, deploy to Azure with CI/CD, optimize performance and scalability, and use AI-assisted tools to accelerate delivery.

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, deploy, and operate scalable, secure AI and LLM-based platform services on AWS. Develop reusable libraries, infrastructure as code, CI/CD pipelines, and observability. Collaborate with applied scientists and product teams to productionize AI capabilities, ensure security and privacy, and drive platform-wide best practices for reliability, cost efficiency, and governance.

Own product vision, roadmap, and strategy for AI digital platforms; manage end-to-end content operations and governance; define OKRs/KPIs; translate business needs into user stories; collaborate with UX/UI, development, analytics, and stakeholders; improve UX and lead planning, testing, and releases.

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.

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.

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.

Work remotely as a Hungarian (hu_HU) C2 speaker to conduct voice-mode conversations with AI chatbots, pursue predefined goals, and rate interactions. Use your smartphone for voice input and a laptop to complete written evaluations in English, following detailed project guidelines and confidentiality rules.

The Principal Data & AI Consultant leads data-related projects, manages revenue targets, supports sales efforts, and coordinates with teams to deliver integrated AI solutions while ensuring client satisfaction and account growth.

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.

We seek a Senior Python Engineer to manage technical objectives, lead development on data pipelines and AI/ML systems, and mentor junior engineers.

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.

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.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

Lead end-to-end data science projects with a product-oriented mindset: engage clients, translate business goals into technical solutions, design and deploy ML/LLM models, mentor team members, drive pre-sales technical discussions, and keep solutions cutting-edge through research and cross-functional collaboration.

Design, architect, and deliver production-grade LLM and classical ML systems. Build and optimize RAG pipelines, distributed GenAI workloads on Databricks, and scalable LLM services. Implement CI/CD, monitoring, evaluation frameworks, lead client technical decisions, and mentor team members.

Work with clients to measure and grow their visibility inside LLMs: define metrics, monitor mentions and sentiment, analyze LLM behavior, turn analytics into actionable content and strategy, support reporting and client communication alongside a senior consultant.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.