Machine learning állások, munkák Budapesten

• Lead client discovery and solutioning
• Own the information design layer
• Design and develop executive and tactical BI solutions
• Elevate client reporting maturity
• Leverage AI tools in daily workflow
• Bridge analysis and communication
• Drive semantic model design and development

• Design agent roles, collaboration patterns, orchestration, models, memory, RAG, tool integration, identity, security, and human control
• Lead end-to-end delivery from discovery and proof of value through production rollout and scaling across business domains

• Improvement of operational efficiency through process optimization, automation, and adoption of new technologies
• Understanding of the company's data warehouse and alignment of data flows with reporting requirements

• Elemzés globális szolgáltatási minőségi adatokon
• Nem megfelelőségi költségek, CAPA, ügyfél-elégedettség, NPS és audit eredmények elemzése
• Minták, ok-okozati csoportok, ismétlődő problémák és kockázatok azonosítása
• Elemzések átalakítása világos következtetésekké és ajánlásokká

• Writing clean, maintainable Python code and automation scripts
• Building CI/CD pipelines and deployment automation for AI systems
• Creating infrastructure as code and configuration management solutions
• Developing APIs, microservices, and event-driven architectures

• Lead the design of AI-assisted SDLC patterns for repository intake, code exploration, build diagnosis, and engineering evidence generation
• Manage and prioritize the product backlog balancing maintenance and modernization efforts

• Design enterprise Data & AI strategies that use Data & Generative AI to drive real business growth
• Translate complex business needs into clear AI solutions that deliver value
• Act as a trusted advisor to C-level leaders on AI adoption, regulations, and ROI

• Design of architectural patterns for transition from legacy monolithic patterns to modern scalable AI-integrated architectures
• Leading the "Strangler Fig" approach for wrapping, refactoring, or replacing legacy components

• Design and maintain automated pipelines for legacy .NET deployments and modern containerized/Kubernetes workflows
• Automate provisioning and management of infrastructure using Terraform, Ansible, or Bicep

• Coordination of operations 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

• Building, configuring and maintaining bare metal hosts and Kubernetes clusters to run GPU/AI workloads
• Designing and operating NVIDIA AI related software stack (Slurm, Run AI)
• Providing customized application support for AI related workloads

Fejlesztés és megvalósítás komplex generatív AI alkalmazásokban a felfedezéstől a bevezetésig stratégiai, taktikai és operatív hatás érdekében...

• 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

• 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

• Development and maintenance of the C++ application and related scripts
• Work in a Linux environment using GCC and common command-line development tools
• Investigation of issues in existing software, identification of root causes, and implementation of reliable fixes

• Maintenance and enhancement of an automated market-making application
• Development and maintenance of C++ and python backend applications and services used in brokerage-related systems
• Investigation of issues in existing software, identification of root causes, and implementation of reliable fixes

• Technical leadership post-sales for strategic customers in big data project design and implementation
• Identification and driving of new initiatives to turn data into actionable business value aligned with business goals

• Operation, maintenance and optimization of production Linux and cloud environments
• Management and support of containerized applications using Docker and Kubernetes/OpenShift
• Monitoring of infrastructure, applications and integrations using modern monitoring and logging solutions

• Technology architect with T Cloud Public hands-on experience
• Deep expertise in public cloud architecture
• Strong understanding of VPC networking
• Strong understanding of IAM & federation
• Strong understanding of cloud security architecture
• Strong understanding of Kubernetes & container platforms

• Development and fine-tuning of AI models including LLMs, Agentic AI, and Generative AI systems
• Building and deploying robust AI applications and services on cloud platforms
• Design and implementation of efficient AI pipelines from data preparation to model deployment and monitoring