Machine learning engineer állások, munkák it programozás, fejlesztés területen

• Design, development, and optimization of core ML models, especially in Computer Vision and Deep Learning
• Development of efficient, scalable training and inference pipelines
• Mentoring junior ML engineers, reviewing code and sharing best practices

• Design and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling including defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize AI and machine learning solutions combining custom models with cloud and third-party AI services
• Assess client needs and data for AI/ML solutions

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....

• Identify, design and evaluate ML and AI approaches for geospatial, Earth observation and unstructured data (e.g. multimodal data fusion), and decide with the team which methods best fit humanitarian challenges
• Keep track of the state of the art in AI and turn new ideas into prototypes and products

Predictive ML: Build models for lead generation, demand forecasting, residual value forecasting, portfolio steering, and fraud detection....

• Develop an efficient and reproducible workflow for application-oriented machine learning solutions.
• Be involved in sample preparation and data acquisition in the laboratory for full data ownership

• Architecture and delivery of production-grade LLM systems and classical ML solutions
• Design, evaluation, and optimization of RAG pipelines
• Building scalable, production-grade LLM services and agentic workflows
• Definition of architecture trade-offs with focus on system-level optimization

• Develop agentic AI applications from refined business and technical requirements.
• Implement agent workflows, prompts, model integrations, memory, RAG, tool calling, security controls, and human-in-the-loop steps.

• Designing, developing, and evaluating machine learning models on complex telecom and retail data
• Transforming high-volume datasets into production-ready solutions such as churn prediction models and point-of-sale fraud models