Python developer állások XII. kerületben középiskolai végzettséggel

• Writing production-quality, well-tested scraper code in Python
• Learning the architecture of the collection platform including scrapers, proxy layer, queues and enrichment API
• Debugging issues across multiple components using logging, monitoring and alerting tools

• Configuration of the P&P platform end to end, across both frontend and backend
• Tuning and optimization of forecast and attribution models to match client data and business logic
• Writing custom business logic in SQL, Python, or Go where standard configuration isn’t enough

Lead the design, development, implementation, validation, and continuous improvement of automation systems that enhance Inventory Control processes and optimize material handling and material flow across manufacturing and warehouse operations including AMRs, AGVs, AGFs, and other autonomous...

• Delivery of software releases on schedule with defined scope and quality
• Transition to continuous delivery
• Ensuring reliable operation of CI/CD pipelines and improving their efficiency
• Contribution to test strategy and coordination of its execution
• Development of test frameworks and automation tools

• Ownership of on-device inference
• Design and implementation of Android inference solutions
• Development and maintenance of native Android vision layer using C++ and Android NDK
• Creation of JNI bindings connecting native components to Kotlin application

• 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

• CI/CD-folyamatok tervezése, felépítése és üzemeltetése (Azure DevOps, GitLab CI, GitHub Actions vagy Jenkins)
• Infrastruktúra kezelése kód formájában (Terraform, Bicep/ARM, Ansible)
• Konténeres környezetek felépítése és üzemeltetése (Docker, Kubernetes / AKS)

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• Embedding in the daily life of up to three engineering teams: Customer Platform and Scoring
• Ensuring technical designs align with SEON's architectural best-practices, are pragmatic and complete
• Ensuring security, scalability, and compliance in all technical designs

• Implementation and optimisation of approved looks
• Collaboration with supervisors, lighters, and compositors to ensure high-quality shot delivery
• Alignment and self-direction of work with schedules and creative expectations

• Creation of 3D rigs for different types of objects, animals and characters
• Collaboration with the visual effects supervisor and Lead CG to determine the best possible technical and creative approaches
• Delivery of work within established project targets and high quality standards

• Odoo-modulok fejlesztése és testreszabása Python 3, XML, HTML használatával
• Backend-szolgáltatások fejlesztése Java/Spring és Hibernate segítségével
• Külső rendszerek összekapcsolása REST- és XML-RPC-API-k segítségével
• Eseményalapú integráció az Apache Kafka segítségével

• Magas szintű technikai és üzleti tanácsadás ügyfelek számára az Atlassian termékeivel és saját fejlesztésű megoldásokkal kapcsolatban
• Ügyfelek üzleti és IT folyamatainak felmérése
• Komplex megoldások tervezése, bevezetése és minőségbiztosítása

• Kutatás és fejlesztési projektek végigkövetése a tervezéstől az átadásig
• Beágyazott vezérlők programozása C nyelven
• Prototípus berendezések beüzemelése
• Prototípus berendezések tesztelése
• Gépész-, villamos- és mechatronikai mérnökökkel való együttműködés
• Dokumentáció írás angolul és magyarul

• 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

• 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

• Partnering with engineering teams to automate and optimize service availability, scalability, performance, monitoring, and alerting
• Developing software to automate the toil required to keep SaaS offerings highly available, scalable, and resilient

• Design and implement data models aligned with industry best practices
• Build and optimize scalable data warehousing and lakehouse solutions and ensure data integrity end-to-end
• Create and own robust ELT pipelines connecting multiple internal and external data sources

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

• Shaping AWS-based cloud architectures and technical decisions for SaaS, PaaS, web application, and enterprise cloud environments
• Design and implementation of Infrastructure as Code solutions using tools such as Terraform, AWS CloudFormation, or Pulumi