Python programmer állás, munka

• Software release delivery on schedule with defined scope and quality
• Transition to continuous delivery
• Reliable operation of CI/CD pipelines
• Improvement of CI/CD pipeline efficiency
• Contribution to test strategy
• Coordination of test strategy execution
• Development of test frameworks and automation tools

• 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

• 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)

• Search, collect, consolidate, and validate financial and non-financial data across departments and systems
• Design, build, maintain, and automate dashboards and underlying data pipelines to provide timely, accurate insights to stakeholders

• Kapcsolattartás az ügyfelekkel, igények felmérése és tanácsadás
• Azure Function App fejlesztés
• Dokumentálás
• Programozás Pythonban
• Azure infrastruktúra tervezés és karbantartás
• Rendszeres ellenőrzés, frissítés és teljesítmény optimalizálás Azure környezetben
• Fejlesztések tervezésében aktív részvétel

• 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

• Client-facing communication
• Participation in pre-sales discussions
• Collaboration with sales and customer success teams
• Communication with clients' technical teams
• Solution design and delivery
• Design and configuration of data labeling and processing pipelines using Python-based tools

• 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....

• 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

• 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

• Creation and preparation of 3D assets, environments, and scene layouts for Omniverse and OpenUSD workflows
• Assembly of digital-twin scenes for industrial, facility, product, or enterprise visualization use cases

• 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

• End-to-end AI solution ownership from problem definition through prototype validation and production support
• Partnership with product managers and business stakeholders to translate real-world problems into data science and AI initiatives

• Development and deployment of AI-powered applications using modern software engineering practices
• Integration of Large Language Models into business solutions
• Building scalable backend services using Python or Node.js
• Development of intuitive user interfaces with React or Angular

• Building AI-assisted development workflows for code understanding, documentation, build triage, and handover preparation
• Implementing workflow utilities, prompt packs, repository analysis scripts, documentation helpers, and integration adapters