Ai és automatizáció állások, munkák

• Lead end-to-end product discovery for player-facing Sportsbook improvements, including new features, UX/UI enhancements and new betting experiences
• Translate strategy and customer problems into clear hypotheses, concepts, requirements and measurable success criteria

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Build, operate, and improve production data pipelines using Python, Airflow, Kubernetes, and related technologies
• Design scalable data pipelines and data models for growing data volumes and processing needs

• Design, build, and maintain large-scale distributed data systems
• Develop data processing pipelines using technologies such as Spark and streaming solutions
• Work on systems that evaluate and ensure the quality and integrity of large-scale knowledge graphs

• Development of a robust framework for generating and distributing work across all operations
• Creation of tools and frameworks for teams to design specific workflows
• Establishment of a comprehensive reference for a 360-degree view of customer data

• Hands-on technical leadership for a team building and operating cloud and platform capabilities
• Shaping technical solutions with Architects
• Building POCs
• Reviewing implementations
• Coaching and unblocking engineers
• Sharing knowledge and promoting collaboration

• Lead end-to-end product discovery for player-facing Sportsbook improvements, including new features, UX/UI enhancements and new betting experiences
• Translate strategy and customer problems into clear hypotheses, concepts, requirements and measurable success criteria

• Design and update of data architecture to ensure a managed and automated process for data flows
• Optimization of data platforms (PostgreSQL) to ensure stable and timely data streams
• Building and continuous optimization of data ETL (Extract, Transform, Load) processes

• Building and leading multiple AI directions within a unified strategy
• Defining and owning the overall AI roadmap and priorities across domains
• Designing scalable AI and ML architectures suitable for production
• Translating business needs into practical and impactful AI solutions

• Technical ownership of assigned areas within Infor ERP LN and related platforms
• Monitoring, troubleshooting, and resolving complex system, technical, and performance issues
• Leading root cause analysis and driving sustainable, long-term solutions

• Development, adaptation, and deployment of end-to-end tracking solutions for sports officiating and analytics
• Implementation and optimization of ML models using Python, C++, PyTorch, NumPy, Pandas, and OpenCV
• Design and building of ETL pipelines and data manipulation workflows

• Definition and improvement of metrics representing product performance and operations efficiency
• Data collection from multiple sources ensuring accuracy and completeness
• Data cleaning, organization, and transformation into usable formats for analysis

• Értékesítési folyamatok műszaki támogatása
• Ügyféligények és műszaki elvárások szakmai értelmezése
• Technikai kérdések megválaszolása és megoldási opciók előkészítése
• Műszaki megoldási koncepciók kidolgozása
• Rendszerarchitektúrák kidolgozása
• Konfigurációs javaslatok kidolgozása

• Tackling complex technical challenges focused on scalability, reliability, and speed
• Maintaining a high-performance infrastructure that processes millions of events with low latency
• Collaborating with Data Scientists and Engineers to design and implement cutting-edge feature topologies

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Lead advanced vulnerability assessments, penetration testing, and threat modeling exercises
• Collaborate with and provide technical guidance to the SOC team on complex threats, targeted attacks, and advanced persistent threat (APT) scenarios

• The work spans three distinct problems, and you keep them clear: testing agentic product behaviour, using agents to test conventional software, and building the agentic quality infrastructure that supports both. 
• Quality engineering 

Ready to fast‑track your career and take on real global challenges? ...

AI-driven vulnerability management. When frontier models can generate thousands of findings in a weekend, the bottleneck shifts from discovery to triage, verification, and remediation. We're building the pipeline that makes this sustainable....

• Real-time decision making with machine learning for risk assessments
• Ownership of risk infrastructure for Instant ACH in the US market
• Cross-functional collaboration with software engineers, data analysts, and data scientists