Python programmer állások II. kerületben (ai és automatizáció)

• Build and maintain scalable cloud infrastructures using modern services and best practices
• Proactive detection, analysis, and fixing of vulnerabilities and performance bottlenecks in cloud environments

• Training and fine-tuning models against curated datasets
• Running systematic hyperparameter and architecture experiments
• Building and maintaining evaluation suites
• Analyzing model failures and translating findings into dataset or training improvements

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments (Azure, AWS, or GCP)
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• Architect and implement scalable AI/ML solutions on Azure or GCP
• Design and build AI solutions for sales use cases such as automated RFP/RFI ingestion, classification, and response generation
• Develop intelligent document understanding for PDFs, Word, Excel, presentations

Automatizálás és digitalizáció manuális hibakeresési, KPI-ellenőrzési és vizsgálati folyamatok kiváltásával okos, automatizált megoldásokkal és scriptekkel...

• Leadership in integration of security best practices into DevOps lifecycle
• Architecture, implementation and continuous improvement of security services, CI/CD pipelines and cloud infrastructure
• Design and enforcement of secure configuration management, secrets handling, RBAC and network policies

• Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses
• Active monitoring and optimization of pipelines for performance and cost
• Configuration of pipeline deployments and management of environment settings using IaC tools and CI/CD

• Komplex AI megoldások fejlesztése ügyfelek számára (chatbotok, service botok, LLM-alapú alkalmazások)
• RAG (Retrieval-Augmented Generation) alapú megoldások fejlesztése Python/LangChain vagy Java/Spring AI technológiákkal

• Lead the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• Ownership of architecture and delivery of production-grade LLM systems and classical ML solutions
• Design, evaluation, and optimization of RAG pipelines including retrieval strategy, chunking, indexing, and monitoring

• 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

• 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

• 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

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...

• Planning, implementation, development and testing of HW/SW components for the company backbone and data center networks
• Preparation of technical feasibility studies, design proposals and execution of POCs for the networks

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management and Cloud-Native Application Protection Platform tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines including IaC scanning and policy-as-code

Agent workflows with LangGraph / LangChain / PydanticAI / CrewAI (routing, retries, fallbacks, timeouts, human-in-the-loop)....

• Technical onboarding of TelCo and OSS applications onto Kubernetes-based platforms
• Contribution to Site Reliability Engineering through automation of operational tasks and creation of enhancements

• Support the integration of security best practices into the DevOps lifecycle
• Contribute to the implementation and continuous improvement of security services, CI/CD pipelines, and cloud infrastructure with a focus on automation and scalability

• Coordination of operations together with Data Center, IaaS and PaaS layers
• Coordination and support of network lifecycle activities including installs, upgrades, changes, and firmware updates
• Management of network interconnections and related documentation