Software development engineer állások XXIII. kerületben középiskolai végzettséggel

• Participation in design, POCs and development of complex tasks
• Ensuring high-quality delivery by developers
• Collaboration with architects to communicate, align and implement architecture direction
• Alignment with other tech leads to ensure technical consistency

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• 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

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• 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

• Monitoring market movements and reacting accordingly
• Assessing and approving bets that exceed client limits
• Tracking and analysing incoming bets with proper tagging
• Implementing risk management strategies
• Reporting betting activity to the Customer Risk Analyst Team

• Banki környezetben lévő java alkalmazásszerverek (Welogic, Glassfish, Websphere) napi működésének feltételei (performancia, üzembiztonság, mentések, monitorozás) biztosítása
• Weblogic, Glassfish és Websphere alkalmazásszerverek folyamatos üzemeltetése és monitorozása

• Chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
• Retrieval pipeline with vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation

• 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

Performing tasks related to the position....

• Operation, maintenance and scaling of database platforms handling terabytes of data
• Design of scalable, reliable database architectures for different workloads and availability requirements
• Support and optimization of databases including PostgreSQL on AWS RDS, ClickHouse, and YugabyteDB

• Design and development of low-level software to integrate new Arm architecture features across Linux, Android, and related userspace components
• Architecture, development, and optimization of core OS subsystems with a focus on performance, power efficiency, and security

• Contribution to the ID Verification ML team building in-house ML solutions
• Delivery of high-quality NodeJS, TypeScript and Python code with simplicity, performance and scalability in mind

• Ownership and evolution of Tulip’s Cypress + TypeScript E2E automation framework
• Integration of automated test suites into CI/CD pipelines
• Research and implementation of load and performance testing strategies
• Application of AI-assisted tools to accelerate test generation and failure triage

• Active involvement in the software design and test of reference application processor firmware
• Low level software development, test and debug on various platforms including software models, development boards and shipping products

• Design and implement data fabric architecture across heterogeneous data sources and platforms
• Develop scalable ETL/ELT pipelines for batch and real-time data integration
• Integrate structured and unstructured data from databases, APIs, SaaS applications, data lakes, and enterprise systems

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

• Ownership of data models and storage architecture behind AI systems across relational, graph, and retrieval-oriented data
• Building and operating APIs, backend services, and pipelines to keep data current and accessible

• Lead AI-assisted software engineering activities for Meridian by designing repeatable SDLC patterns
• Anchor the engineering approach for OpenStack-derived services, repository-scale understanding, and sovereign software handover readiness

• Industrialization of AI-assisted engineering workflows for Meridian
• Packaging repeatable patterns supporting codebase intake, dependency extraction, build triage, documentation generation, and technical evidence creation across cloud software work packages