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

• Development and operation of data pipelines
• Building data layers in OneDataLake (ODL) Central Data Store Consumer (CDSC)
• Balancing business and IT demands to build modern and efficient pipelines in public cloud environments
• Integration of data preparations into managed processes

• Csapatmunkában történő részvétel jó minőségű, hatékony üzleti alkalmazások létrehozásában
• A rendszertervezéssel kapcsolatos egyeztetési és dokumentációs feladatok ellátása
• Komponensek működési céljainak meghatározása, valamint a kimeneti követelmények és formátumok értékelése

Shaping and evolving the architecture of MDO network automation capabilities including reusable automation patterns, interfaces and technical guidelines...

• Leading and mentoring a team of ML engineers
• Owning model quality end-to-end from experimentation to production-ready artifact
• Conducting regular 1-on-1s for career development and feedback
• Removing blockers and ensuring team well-being

• Building and operating Kubernetes platforms on Open Telekom Cloud
• Managing Infrastructure as Code with Terraform and Terragrunt
• Implementing GitOps delivery using ArgoCD
• Operating PostgreSQL and Apache Kafka on Kubernetes
• Integrating security controls into CI/CD pipelines

• Active partnership with sales and project managers to estimate, design, develop and deliver solutions
• Continuous communication with domain experts and product owners to understand core business needs and goals to synthesize requirement and quality standards

Work directly with clients, product owners, architects, and development teams to understand business and technical requirements and translate them into infrastructure and cloud architecture requirements...

• Use formal verification algorithms to formally prove the correctness of complicated logic problems
• Identify key behaviors for verification to write clear testplans for complex designs
• Implement testplans using innovative formal techniques
• Drive tools to realize their best performance

• Development, governance, deployment, and enhancement of liquidity analytics models and supporting infrastructure across asset classes
• Driving the analytics product lifecycle including prioritization, roadmap decisions, and deployment into production

• Design and build innovative AI solutions combining software development and data engineering
• Collaborate with global technologists, data scientists, and industry experts to transform businesses through analytics and AI
• Develop scalable data pipelines for machine learning and AI applications

• Deployment and scaling of EcliptOS platform
• Ownership of EcliptOS platform delivery lifecycle
• Deployment and configuration of production-grade systems across cloud and hybrid infrastructures
• Validation of performance and stability of deployed systems
• Ensuring structured handovers for long-term success

• Delivery of agentic solutions across multiple client deployments
• Building and configuring full-stack AI-enabled solutions
• Embedding with client service teams and client teams to understand needs
• Translating client needs into technical and functional requirements

• Working across the software stack including firmware, operating systems, device drivers, compilers, runtimes and middleware
• Developing high-performance software for Arm’s advanced architectures, CPUs, system IP, and integrated sub-systems
• Exploring firmware and OS interactions

• System IP microarchitecture specification and design
• SystemVerilog RTL verification and debug
• Formal bring-up of non-trivial IP blocks to achieve required quality levels

• Skálázható cloud-native adatarchitektúrák tervezése Databricks vagy MS Fabric platformokra
• Adatmodellek, sémák és tárolási minták kialakítása (lakehouse, Delta, Parquet)
• Batch és streaming adatok betöltési pipeline-jainak megtervezése
• ETL/ELT folyamatok és transzformációs stratégia meghatározása

• Build and maintain dataset ingestion, cleaning, and versioning pipelines
• Design and operate labeling workflows including quality control and inter-annotator agreement tracking
• Own dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, kubernets, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks before they become threats

• Producing high quality code for the full technological stack from database to UI
• Assisting with troubleshooting basic issues and improving application performance
• Documenting and demonstrating solutions with a focus on security and performance

• Design and implement cloud security controls across AWS, Azure, and GCP
• Build security automation using CI/CD pipelines, Infrastructure as Code, and Policy as Code
• Develop automated compliance checks and remediation workflows

• Analysis and research of cloud-native tools and frameworks for Google Cloud Platform solutions
• Support for the T-Systems Public Cloud Product Management Team in building and defining solutions