Python programmer állások XII. kerületben (it programozás, fejlesztés)

• Design and implement automation for storage provisioning, capacity management, and operational workflows using Python, Bash, and IaC tooling
• Drive reduction of manual toil through scripted workflows and self-service enablement

• 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

• 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

• 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

• 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

• 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

•Working closely with Test Engineers and Developers to understand product requirements and ensure high quality in releases •Testing Siemens SIMATIC S7 PLC and HMI product families’ engineering systems and hardwares •Executing test cases (manual and/or automated) and analyzing results •Writing and...

• Design, implement, and optimize machine learning models to solve complex business problems
• Oversee the entire machine learning lifecycle from data collection and pre-processing to model deployment and monitoring

• Design, build and deploy LLM-powered pipelines and autonomous agents
• Scope and deliver AI solutions with stakeholders
• Set best practices around MLOps and production-grade AI
• Mentor developers and contribute to internal knowledge-sharing

• Manage and support Linux (Oracle Linux, RHEL) and Windows Server infrastructure environments.
• Administer Active Directory services, including DNS, DHCP, Group Policy, and identity management.
• Monitor, troubleshoot, and optimize system performance, availability, and reliability.

• 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

• Design and implement automated test cases for backend, frontend, or API-based systems
• Collaborate with distributed teams across multiple time zones
• Participate in requirement analysis to ensure testability and clarity

• 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
• Partnership with product managers and business stakeholders for problem translation
• Independent planning and execution of research and experimentation
• Design of AI solutions with system-level perspective

• Identify AI and automation opportunities across business processes
• Work with different departments to understand processes, pain points and repetitive activities
• Challenge existing ways of working and propose simpler, automated or completely new approaches

• Working with cutting edge technology enabling cloud journey
• Managing Infrastructure as Code for parts of the Core Platform Layer including Kubernetes Clusters, Message Broker backends, Infrastructure tooling

• Új funkciók és termékek manuális és automatizált tesztelése
• Python alapú automatizált tesztek fejlesztése és karbantartása
• Regressziós tesztek végrehajtása és folyamatos bővítése
• Tesztesetek készítése új funkciókhoz az ügyfélhibák alapján
• Exploratory és use case alapú tesztelés

• 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

• Working with cutting edge technology enabling cloud journey
• Managing infrastructure as code for parts of core platform layer including Kubernetes clusters, message broker backends, infrastructure tooling

• Be able to manage small teams to deliver analytic functions in support of strategic and business development projects.
• Analyse and assess macroeconomic, energy market and other industry-specific developments by synthetizing available empirical evidence, related literature, and economic theory.