Top Artificial Intelligence Jobs in Budapest

Build and maintain an internal agentic-AI platform for engineers: design integrations (source control, CI/CD, observability), define agent architecture, evaluate and deploy models, improve onboarding and discovery, measure adoption, and drive developer enablement and safety practices.

Configure and maintain commercial systems and AI platforms (Salesforce, Claude, Zapier, n8n) to automate GTM workflows; build Salesforce Flows and automations; manage day-to-day admin tasks like user provisioning, reports, and data cleanup; create documentation and train teams on AI-driven workflows while ensuring policy compliance.

Lead a senior engineering team to build production LLM systems at Wise, shaping the AI technical vision and collaborating across functions.

Design and build LLM-based AI pipelines and prototypes (RAG, embeddings, vector DBs, knowledge graphs); evaluate multiple LLMs; run end-user interviews; integrate successful prototypes into CI/CD workflows and productize solutions to improve code development efficiency.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

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.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

The role focuses on creating and implementing Data & AI strategies, advising executives, and managing projects to drive AI adoption and business growth.

As a Senior Software Engineer, you'll lead design and development of scalable components for the Wise Assistant, innovate with AI, and mentor engineers.

Lead design, development, and implementation of digital and AI solutions for Controllers. Collaborate with Finance and Technology, drive cross-functional initiatives, provide SME advisory, ensure scalable adoption, and promote digital upskilling with risk-and-controls focus.

Create and edit video content, motion graphics, and animations for marketing and communications across digital and offline channels. Develop storyboards, visual concepts, and assets using Adobe Premiere Pro and After Effects and AI tools. Collaborate with creative leadership and global stakeholders to produce brand-focused short-form and campaign media.

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.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Lead developer engagement for firmwide AI coding tool adoption: run demos/workshops, create guidance and enablement content, gather feedback, drive responsible practical adoption, advocate for developers, and shape rollout strategy across engineering, platform, risk, and leadership.

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.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

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.

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

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

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.

Design and prototype AI-powered product experiences from discovery to delivery. Conduct research, usability testing, and data-driven validation. Create wireframes, prototypes, and design documentation (Figma). Collaborate with PMs, engineers, and design system teams to ensure accessible, consistent, and user-centered solutions, and raise UX maturity across the organization.

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 end-to-end data science projects with an AI/product mindset: engage clients, translate business needs into technical solutions, drive pre-sales discussions, integrate ML/LLM into products, mentor team members, and stay current with AI/ML advances.

Design, architect, and deliver production-grade LLM and classical ML systems. Build and optimize RAG pipelines, distributed GenAI workloads on Databricks (Spark, MLflow), implement CI/CD and monitoring, evaluate model quality and hallucinations, lead technical decisions in client engagements, and mentor team members while contributing to pre-sales architecture discussions.

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.