Python programozó diákmunka állás, munka

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• Design, build, and operate the firm’s AI platform including shared services, APIs, SDKs, and tooling
• Drive architecture across scalability, reliability, security, developer experience, and cost

• Build and evolve CI infrastructure validating performance methodologies across cloud, on-prem, and hardware targets
• Design controlled, reproducible test environments for distinguishing real regressions from environmental noise

Development and enhancement of the MSCI Index Development Platform and the IndexMetrics Real-Time application using modern software engineering practices, Python, Azure, and AI-assisted development tools...

• 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

• Maintaining and evolving data structures
• Building new ETL flows and maintaining or improving existing ones
• Designing new data structures and data modelling concepts
• Ensuring data quality by verifying data consistency and accuracy
• Creating data validation methods

• 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

• 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, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• 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

• CI/CD pipeline-ok kialakítása, fejlesztése és karbantartása
• Deployment és release folyamatok automatizálása
• Cloud infrastruktúrák üzemeltetése és optimalizálása
• Konténerizált környezetek kezelése (Docker, Kubernetes/OpenShift)
• Monitoring, logging és alerting rendszerek kialakítása és üzemeltetése

• Develop and maintain scalable web applications using Angular or React
• Build and optimize RESTful APIs and microservices using Node.js, Python or .NET
• Define and manage cloud infrastructure using Infrastructure as Code, such as Terraform or OpenTofu

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

• 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

• 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

• 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 Terraform, AWS CloudFormation, or Pulumi

• 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