Python developer állás, munka

• Design and run Kubernetes environments optimized for AI inference, retrieval, experimentation, and agent execution in secure or isolated settings
• Deploy and operate open-source or open-weight model stacks, model gateways, vector databases, and supporting platform components

• 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

• Design, build, and maintain robust CI/CD pipelines
• Manage and optimize cloud infrastructure on T Cloud Public, ensuring high availability and performance
• Administer and troubleshoot Linux and Windows systems in production environments

• Work with an agile mindset and according to agile methodology
• Collaboration within the team, with other teams in the Domain and Deutsche Telekom, and with software engineers using the cloud platform
• Approach work with a DevOps and continuous improvement mindset

• Design and develop scalable cloud security automation solutions across multi-cloud environments
• Build and maintain platform services using modern engineering practices and a code-first approach
• Own and enhance CI/CD pipelines that enable secure, reliable and efficient delivery

• Cooperation with software teams to establish and implement quality processes at every stage of the development cycle
• Development of automated test cases for firmware and Linux-based systems including bootloaders, kernel, device drivers and applications

• Design, build, and maintain modern web applications across the full stack
• Develop rapid prototypes and proof-of-concepts to validate business ideas
• Build AI-powered applications using modern LLM technologies, APIs, and cloud services

• 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

• Build and evolve CI infrastructure validating performance methodologies across cloud, on-prem, and hardware targets
• Design controlled, reproducible test environments to distinguish real regressions from environmental noise
• Build synthetic workloads and targeted microbenchmarks including Arm assembly

• Defining and communicating architectural vision for the Cloud Management Portal aligned with product strategy and customer outcomes
• Making pragmatic architectural decisions balancing modern practices with delivery speed and customer value

• Design of reliable cloud architectures
• Partnership with customers, SRE teams, and internal stakeholders
• Championing automation
• Building reusable Infrastructure as Code templates and utility tools
• Design of secure cloud networks
• Driving customer success
• Operating platforms at scale

• Development and operation of Keycloak/RHSSO-based IAM capabilities on Kubernetes
• Standardization of deployment, configuration, and troubleshooting practices
• Continuous improvement of reliability, security, and automation around the IAM platform

• Experience in different data science topics
• Independent delivery of data science projects with product-oriented mindset
• Engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Active involvement in the software design and test of reference application processor firmware
• Low level software development, test and debug on various platforms including software models, development boards and shipping products

• Designing, implementing, testing, and optimizing reliable, efficient, and high-quality data pipelines
• Automating the extraction, loading and transformation of data from a wide variety of sources
• Developing solutions for the automated acquisition of data from source systems

• Design of complex and scalable data solutions on the AWS cloud platform
• Development and implementation of complex and scalable data solutions on the AWS cloud platform
• Design and optimization of data pipelines
• Writing clean, efficient and maintainable SQL and Python code

• Architect scalable inference services, APIs, and backend components for model-driven and LLM-powered product features
• Productionize AI and ML workflows with strong MLOps practices
• Define and implement evaluation frameworks for model quality, system reliability, latency, and cost

• Design, build, and ship production retrieval systems
• Own vector store selection and operation including scalability, latency, reliability, cost, and multi-tenant design
• Build and operate indexing and refresh pipelines including chunking, embedding generation, backfills, deletes, and versioned indices

Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers...

• 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