Python developer állások XIV. kerületben (ai és automatizáció)

• Architect and lead the development of large-scale ML systems and model pipelines
• Introduction of new ML systems such as traffic sign detection, building facade detection, and other computer vision-based solutions
• Definition and driving of the ML technical roadmap in alignment with company objectives

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

• 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 tools such as Terraform, AWS CloudFormation, or Pulumi

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• Design process of a SoC by integrating the latest Arm IP products
• Development of RTL code using System Verilog
• Running EDA tools to check the design and analyze results
• Using AI-assisted engineering tools to support development and debug
• Identifying and fixing bugs

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize AI and machine learning solutions combining custom models with cloud and third-party AI services
• Assess client needs and data for AI/ML solutions

• Design and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling including defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• 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

• Maintenance of the Tulip Operating System including building, packaging, and support
• Configuration and maintenance of routing, networking, and device configurations
• Maintenance and addition of bootloader, kernel patches, and device trees on Tulip hardware
• Porting the Tulip OS to other hardware targets

• Client-facing communication
• Participation in pre-sales discussions
• Collaboration with sales and customer success teams
• Communication with clients' technical teams
• Design and configuration of data labeling and processing pipelines
• Combination of multiple data labeling components into automated workflows

• 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

• Fejlesztés és telepítés AI-alapú alkalmazások
• Integráció Large Language Model-ekkel üzleti megoldásokban
• Skálázható backend szolgáltatások építése Python vagy Node.js használatával
• Felhasználóbarát felhasználói felületek fejlesztése React vagy Angular segítségével

• Labeling of image data for training of object recognition
• Programming of data acquisition pipelines in Python or C++
• Frontend development of a browser client in Three.js
• Design of 3D props and environments in Blender and Omniverse
• Linux server administration, particularly DGX Spark and Jetson

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

• 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

• Development and implementation of complex generative AI applications from discovery to deployment
• Establishment of best practices framework for deploying cloud architectures supporting AI agents
• Creation of RAG/LLM evaluation pipelines, embedding strategies, and prompt/runtime optimization

• Design and implement modern data architectures and lakehouse solutions
• Provide technical leadership throughout the project lifecycle
• Collaborate with business stakeholders
• Create effort estimations
• Provide technical expertise during the pre-sales phase
• Mentor 3-6 person project teams

• 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

• Managing and migrating ETL pipelines between various source systems
• Validating pipeline build success and output data quality
• Communicating with business stakeholders and SME's regarding their respective data domain
• Analysing and resolving blockers and issues with ETL pipelines and data discrepancies