Top Machine Learning Jobs in Hungary

The Account Executive will drive new business by selling SaaS solutions to Managed Service Providers in the Nordics, managing the full sales cycle, and achieving revenue targets.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Lead enterprise-wide AI strategies, develop GenAI solutions for banking, engage with C-level executives, and drive project execution for impactful AI integration.

The Senior Data Scientist will lead projects, provide consultancy in data science and AI, develop internal solutions, and mentor team members while ensuring customer success.

The Refining and Petrochemical Process Optimization Engineer analyzes process data, works on AI-driven optimization models, and collaborates with clients to improve operations and drive efficiency.

As a Senior Data Scientist, you will build and deploy fraud detection models, collaborate with teams to enhance claim processes, and utilize various data types to improve decision-making and fraud prevention.

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

Lead the LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

The Senior Solutions Architect (AI) designs solutions, engages with customers, supports sales efforts, and bridges the gap between sales and delivery within AI and data science projects.

The AI Solution Architect will design AI and ML solutions, oversee pre-sales processes, create project estimates, and validate proposals, ensuring technical accuracy and client engagement.

The role involves designing and implementing machine learning solutions, managing projects from idea to deployment, and optimizing models using AWS and Docker.

The Senior Machine Learning Engineer will develop and optimize multimodal understanding models, focusing on computer vision, while leading technical projects and driving architectural decisions for model development.

Manage IT projects, oversee project deliverables, mitigate risks, ensure stakeholder satisfaction, lead agile development, and mentor team members.

As a Backend Engineer, you'll lead technical problem-solving in data engineering and machine learning, improving risk assessment tools and features.

Lead the design and implementation of Energy Management Systems, align strategies for energy optimization, and ensure integration with digital platforms.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

The Head of Data will define and implement the data strategy, build infrastructure, and lead a team to drive data-informed decisions and growth across the company.

Responsible for project delivery, managing a team, improving solutions, handling customer relationships, and ensuring project alignment with business needs.

The AI Senior Product Manager will lead AI product strategy, manage the AI roadmap, and collaborate with cross-functional teams to deliver innovative solutions.

Lead the modernization of C++ Computer Vision library, design algorithms for Intelligent Document Processing, and mentor engineers.

This entry-level role involves assisting in AI/ML projects while developing foundational skills in Python, machine learning, and cloud technologies. Responsibilities include coding, research, model training, and participating in agile ceremonies under supervision.

N-iX seeks a Data Engineer with MLOps experience to create scalable ML pipelines, deploy models, and contribute to best practices in data engineering and coding.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As an AI/ML Specialist Solutions Architect, you will design scalable AI solutions, manage AI deployments, and collaborate with clients to enhance customer satisfaction.

Lead AI/ML architecture discussions and implement large-scale AI solutions. Mentor engineers while influencing technical standards and championing MLOps best practices.