Top Artificial Intelligence Jobs in Budapest

Join Welo Data’s contributor network for flexible, remote AI projects involving text, image, or audio annotation; search, ad, and chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Candidates need native or near-native Kannada fluency, English proficiency, digital tool comfort, and strong attention to detail. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Join Welo Data’s global contributor network for flexible, remote AI projects involving text, image, and audio annotation; search, advertisement, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. Projects vary from several days to weeks, with scope, timelines, and compensation provided before each assignment. Native or near-native Hebrew, English proficiency, digital tool comfort, and strong attention to detail are required.

Join Welo Data’s Cantonese AI Trainers Network to support AI data annotation, evaluation, and prompt creation. Work remotely with flexible hours while contributing language and cultural expertise to improve AI systems. Applicants should be native or near-native Cantonese speakers living in Hong Kong, Macau, mainland China, or Malaysia, with strong English skills, digital tool proficiency, attention to detail, and curiosity.

Join Welo Data’s Vietnamese AI contributor network for potential remote projects involving text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. This is not an active opening. Projects vary in duration and compensation, with details provided before participation.

Join a remote global contributor network supporting AI data projects. Depending on project needs, you may annotate content, evaluate search results or chatbot responses, create prompts, and verify cultural and linguistic accuracy. Projects range from a few days to several weeks, with scope, timelines, and compensation provided before participation.

Lead enterprise AI architecture, MLOps, and model governance on Databricks. Define standards for model development, deployment, monitoring, and explainability. Drive production-grade AI solutions (forecasting, NLP, RAG, anomaly detection), mentor teams, and align AI with data platform and business needs.

Lead the architecture and production delivery of AI agents and intelligence systems for payments. Build reliable, low-latency LLM applications with tool use, orchestration, memory, RAG, multilingual conversations, evaluation, observability, and safety guardrails. Develop merchant intelligence and AI operations automation, mentor engineers, establish technical direction, and collaborate across Product, Data Science, and engineering teams in a compliance-sensitive global environment.

The AI / Machine Learning Researcher will conduct applied research, develop prototypes, and work with data to create AI-driven features related to home design.

Build and maintain production-grade agentic AI systems for an internal AI platform. Design AWS infrastructure, knowledge graph solutions using Neptune or Neo4j, and LLM-based reasoning and retrieval workflows. Troubleshoot integrated systems, collaborate across teams, influence technical direction, and support engineering best practices. The contract is expected to last 4–6 months, with possible extension, and requires strong Python, AWS platform engineering, and agentic AI expertise.

Build and deploy production-grade LLM capabilities for the Kayzen Console. Responsibilities include developing AI-backed APIs and full-stack features, implementing RAG, embeddings, agents, and tool calling, and establishing evaluation, monitoring, observability, and feedback systems. The role optimizes quality, latency, reliability, and cost; troubleshoots production issues; collaborates with Product, ML, and Engineering; and turns prototypes into maintainable AI products.

Leads global privacy and AI legal matters for a SaaS employment platform. Advises Product, Engineering, Security, and business teams on privacy by design, AI governance, data protection, workforce data, incidents, regulatory compliance, vendor reviews, transfers, and contract terms. Builds scalable policies, controls, templates, training, and customer-facing trust materials while monitoring global privacy and AI regulations.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Develop and optimize GPU- and CPU-accelerated numerical software libraries for AI, analytics, image processing, computer vision, and scientific simulation workflows. Responsibilities include designing scalable algorithms, performance tuning, benchmarking across architectures, collaborating on feature requirements and technical roadmaps, providing technical leadership, and improving library usability and performance.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Design, build, and optimize cloud-based Databricks data platforms and scalable ETL/streaming pipelines (Delta Lake, Spark). Collaborate with data scientists and stakeholders, troubleshoot performance, and support analytics and MLOps workflows on Azure.

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.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.