Top Artificial Intelligence Jobs in Budapest

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will optimize model serving and inference architectures, focusing on high-performance, resource-efficient solutions for AI systems across various deployment environments.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead end-to-end client engagements to measure and grow brokerage visibility in LLMs: set up LLM visibility metrics, analyze where partners appear versus competitors, define and execute growth experiments (content, video, social), and deliver clear, actionable reporting and strategy. Own scoping, recurring reporting, and client relationships on a fast-moving, data-driven team.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead data quality, investigation, remediation and automation for Workday Skills and Job Architecture. Use Python and SQL to profile and fix HR datasets, build data quality pipelines in Snowflake, create monitoring dashboards, and partner with stakeholders to ensure AI-ready data and governance compliance.

The Principal Software Engineer will lead AI initiatives, shape engineering architecture, mentor engineers, and guide technical decisions in a sizable organization, focusing on AI governance and platform design within AWS.

The AI Platform Architect leads enterprise architecture for cloud and hybrid platforms, defines API and data governance strategies, oversees AI/ML infrastructure, and mentors engineering teams while ensuring compliance and security.

The AI Engineer will design and develop AI solutions, focusing on proof of concepts using GenAI platforms, manage projects, and ensure Agile delivery.

Own and scale a large international Google Ads account end-to-end using AI (Claude) as a co-pilot. Responsibilities include keyword research, ad copy and localization, campaign structure, compliance and appeals, bid/CPA/ROAS management, monitoring and reporting, and launching growth across countries, products, and devices.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Manage and optimize the GTM technology stack (Salesforce, Marketo, Outreach, Gainsight), lead integrations, automate processes, support and train GTM teams, build reports/dashboards, manage vendors, ensure data governance and run GTM systems projects from design through deployment.

Drive adoption of Legal Operations tools by creating training, onboarding journeys, guides, and live sessions. Enable and apply AI to improve legal workflows, identify use cases, train users, monitor adoption metrics, collect feedback, and support global rollouts and stakeholder coordination.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Design and maintain CI/CD, repository governance, secrets and access management, observability, and deployment automation for .NET-based AI agents. Collaborate with AI engineers using Claude/OpenAI, ensure security, compliance (GDPR), operational readiness, and support multiple development teams to move AI agent solutions into production.

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.

Lead the development of complex data pipelines and AI/ML systems, manage technical objectives, mentor junior engineers, and drive architectural decisions.

As an AI Solutions Architect, you will design scalable AI architectures, evaluate technical feasibility, define product requirements, provide mentorship, and assess AI models for enterprise deployment.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.