Engineer ai állások, munkák it programozás, fejlesztés területen

• Translation of AI opportunities into executable technical solution designs
• Definition of integration approaches with IT and business application/data owners
• Implementation of AI use cases using enterprise AI services and LLM APIs

• Building and maintaining simulation environments for mobile robotic perception and navigation
• Developing and integrating robotic behaviours and learning pipelines for reinforcement learning and motion planning in complex outdoor environments

• You develop and industrialise end-to-end machine learning algorithms for ADAS functions.
• Furthermore, you design and improve human-like driving behaviour using behaviour models, reinforcement learning, and hybrid data- and model-driven approaches.

• You develop and operate scalable, secure, and high-performance AI platforms in modern cloud environments.
• Additionally, you support the entire AI lifecycle – from data preparation through development and training to the productive operation of machine learning and generative AI solutions.

• You will integrate models that run on edge devices within the infotainment domain.
• Furthermore, you will prototype & build new GenAI experiences, turning ideas into reality as refined features that will be launched to our customers.

You build and operate cloud-based validation environments for a multi-agent system that operates across ADAS, Infotainment and backend-service domains – defining test architectures, scenario pipelines, behavioural oracles and regression suites....

• Working closely with product managers to understand customer needs
• Shaping new features and turning requirements into clear test plans
• Identifying potential risks
• Improving automation frameworks
• Supporting reliability, scalability, and security of micro-services behind SaaS platform

• Development of integrated automated tests
• Creation of automated test reporting and metrics collection
• Transition of testing from manual to automated approach
• Maintenance and refactoring of testcases
• Collaboration and knowledge sharing with QA team members and software developers

• Architect and lead the development of large-scale ML systems and model pipelines
• Introduce new ML systems such as traffic sign detection, building facade detection, and other computer vision-based solutions
• Define and drive the ML technical roadmap in alignment with company objectives

• Development and improvement of football ball and player tracking algorithms using multi-camera systems
• Contribution to multi-view geometry solutions for accurate tracking across the field of play
• Support for approaches to handling occlusion and recovery in fast-paced sports scenarios

• Definition and development of system-level tests for high-end embedded computing systems
• Automation of test execution and evaluation through scripting in a CI environment
• Increase of test coverage by extending functional examples into comprehensive test suites

• Research, develop, implement, and test artificial intelligence algorithms to improve the quality and robustness of the static world model in automated vehicles
• Focus on road structure including lanes, curbs, and drivable trajectories

• Design, develop, and maintain scalable fullstack applications using .NET and Angular
• Integrate and manage messaging systems such as Kafka and RabbitMQ
• Deploy and manage applications using Docker and Kubernetes in AWS environments
• Work with various databases including PostgreSQL, MSSQL, and Couchbase

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

• Ownership of supervisor approval workflows
• Design and evolution of backend systems for file transfer routing
• Development and maintenance of audit logging and compliance capabilities
• Leveraging AI-assisted development tools for engineering workflows

• Design, build, and maintain build a secure and performant unified Edge platform for all partner-facing and customer-facing traffic.
• Bring a strong engineering perspective to the team, helping to instill software design best practices, scalable system architecture, and high-quality code standards.

• Providing technical assistance and guidance to data engineering and machine learning community
• Owning difficult technical problems
• Participating in the vision for data engineering through documents and conversations
• Getting buy-in from stakeholders and leadership

• Solve concrete engineering problems around latency, throughput, consistency, data durability, and storage cost
• Own parts of the system end-to-end, from design through production rollout
• Shape the team's technical direction through design reviews, prototypes, and honest debate

• Cooperation with software teams to establish and implement quality processes at every stage of the development cycle
• Development of automated test cases for firmware and Linux-based systems including bootloaders, kernel, device drivers and applications