Top Artificial Intelligence Jobs in Budapest

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.

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.

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.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

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.

Design and deploy AI microservices and LLM-powered applications using Python, REST/gRPC, Docker, and Kubernetes. Build RAG, hybrid search, agent, fine-tuning, quantization, evaluation, and prompt-guardrail systems. Monitor model quality, latency, cost, and security while integrating databases and SIEM/SOC platforms. Establish MLOps, testing, observability, and compliance standards for high-security public-safety environments. Collaborate with security teams on CJIS and FedRAMP audits, with optional travel to Japan and the United States.

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.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

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.

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.

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.

Design, build, and ship AI-powered product features from prototype through production. Own architecture, model selection, integrations, evaluation, reliability, cost, and latency. Work across frontend, backend, and infrastructure while establishing reusable AI development practices. Collaborate with engineering and product teams to define requirements, identify risks, and deliver maintainable, monitored, production-ready systems using LLMs, embeddings, RAG, vector databases, and multiple providers.

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.

Create narrative-driven, long-form cinematic videos using AI-first workflows, from scripting and prompting through generation, editing, compositing, motion graphics, color correction, and final delivery. Maintain character and scene consistency across sequences, experiment with emerging AI video models, refine production workflows, and independently execute creative direction. The role includes stylized and explicit adult content and requires strong storytelling, technical judgment, rapid iteration, and collaboration.

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.

Perform independent price verification, valuation adjustments, and fair value leveling for global spread products. Reconcile front-office marks with independent valuations, investigate discrepancies, prepare reports for senior management and regulators, and collaborate with traders, risk, and controllers to resolve valuation issues while ensuring robust controls and documentation.

Build production-grade AI applications, APIs, internal tools, dashboards, agents, and automation solutions using Python and Generative AI. Partner with stakeholders to identify automation opportunities, design scalable architectures and data models, establish responsible AI frameworks and guardrails, and prototype solutions rapidly. Review and optimize AI-generated code, integrate modern AI frameworks and retrieval systems, and mentor stakeholders on safe AI-assisted development.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

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.

Owns planning, activation, optimization, testing, budgeting, and reporting for paid campaigns across TikTok, Meta, and YouTube. Manages $1M+ budgets, presents data-driven recommendations to clients, tracks platform changes, and partners with strategy and creative teams. The role also mentors a Paid Media Executive and establishes team standards for campaign QA, reporting, naming conventions, and optimization practices.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.