Top Artificial Intelligence Jobs in Budapest

Leads enterprise AI architecture and solution shaping for private, sovereign, hybrid, and cloud deployments. The role combines customer-facing pre-sales, technical discovery, presentations, proposals, architecture design, and coordination across cloud, infrastructure, networking, security, data, and application teams. Responsibilities include defining AI platforms, RAG and vector search architectures, model hosting, agentic workflows, governance, observability, reference architectures, and implementation roadmaps.

Build, deploy, integrate, and troubleshoot enterprise AI platforms across on-premises, private cloud, hybrid, and public cloud environments. Develop LLM, RAG, vector database, embedding, and agentic AI prototypes; translate customer needs into technical architectures; lead proof-of-concepts, demonstrations, workshops, and early implementations; and create reusable deployment assets while collaborating with infrastructure, security, networking, sales, and delivery teams.

Lead 0-to-1 product development for Kimchi’s AI coding and infrastructure products. Responsibilities include identifying high-value customer problems, conducting discovery, validating hypotheses, prioritizing opportunities, partnering with engineering and design, defining success metrics, driving adoption, and communicating product direction. The role requires strong judgment in ambiguous, rapidly changing technical markets and hands-on use of modern AI tools.

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

Develop a highly optimized, open-source inference framework for AI networking acceleration on supercomputers and data centers. Build low-level infrastructure using hardware offloads, GPU kernels, RDMA networking, and performance optimization techniques. The role requires modern C++/C/Rust development, Linux expertise, TCP/IP knowledge, and understanding of computer architecture and operating systems, with CUDA, ML, LLM, parallel programming, HPC, and RDMA experience preferred.

Support development of AI and perception solutions for camera and radar products used in autonomous driving and safety-critical vehicle systems. The intern will work with innovative software technologies, collaborate in an agile international team, and receive mentoring while contributing to autonomous mobility projects.

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

The Senior Data Scientist will own AI projects, prototype LLM solutions, evaluate AI performance, and mentor junior data scientists.

Own paid acquisition across Google, Bing, Meta, and YouTube, scaling campaigns from creative production through launch, optimization, and attribution. Produce high-volume ad assets using AI tools, manage full-funnel campaigns, improve campaign processes, automate repetitive work, and analyze CAC, ROAS, payback, and product activation data. The role requires hands-on Google Ads expertise, creative production skills, subscription funnel experience, and strong ownership in a remote startup environment.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Leads AI transformation across automotive operations by redesigning value streams, embedding AI into processes and decision-making, defining data and solution requirements, and coordinating cross-functional implementation. The role develops transformation roadmaps, identifies data and governance gaps, drives stakeholder adoption, supports enablement, and tracks value realization across functions including supply chain, manufacturing, procurement, finance, and engineering.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive intelligence, sales enablement, technical content, and cross-functional collaboration. The role targets enterprise buyers through sales-led motions and requires measurable pipeline impact, strong writing and presentation skills, and comfort working with technical teams. Experience in AI, contact centers, QA, testing, observability, or monitoring is advantageous.

Leads enterprise AI adoption programs from assessment through workshops, hands-on enablement, adoption measurement, and team self-sufficiency. Builds client relationships, develops internal AI champions, creates reusable prompts and templates, ensures compliance with data-governance and responsible-AI policies, reports business impact and ROI, and identifies expansion opportunities. The role requires practical AI fluency, strong facilitation, change-management expertise, customer success capabilities, and executive communication skills.

Build and operate Kraken’s internal AI platform, including governed integrations, authentication, internal tooling, evaluation systems, spend controls, identity and access controls, provisioning, vendor configuration, and policy enforcement. Provide technical enablement through documentation, deep dives, and office hours while supporting teams and administering the enterprise AI estate.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

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.

Deliver live workshops and applied labs on AI-assisted visual, video, document, and interactive content production. Demonstrate tools and workflows, guide learners through hands-on projects, provide structured feedback, and help adapt processes to professional environments. Collaborate with the course team to develop templates, exercises, checklists, and case studies while continuously improving course content based on learner feedback and AI industry developments.

Provide EMEA-wide legal advice on data protection and GDPR compliance, develop and apply group data compliance frameworks, review processing activities and agreements, manage incident response and audits, deliver training, monitor EMEA privacy laws, and support cross-border transfers and global projects.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

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.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.