Python programme állások Budapesten (ai és automatizáció)

• Design and development of GIS-based solutions, web applications, dashboards, and reporting solutions using Esri technologies
• Analysis and optimization of existing GIS processes focusing on scalability, data quality, and operational efficiency

• AI-Native Infrastructure: Modern, GPU-intenzív AI/ML keretrendszerek szoftveres alapjainak megtervezése és kiépítése.
• Deep Containerization: Komplex Docker image-ek faragása, kifejezetten GPU-kompatibilis, AI workload-specifikus erőforráskezelésre (NVIDIA-docker, CUDA optimalizáció).

• 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

• 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 implement automation for storage provisioning, capacity management, and operational workflows using Python, Bash, and IaC tooling
• Drive reduction of manual toil through scripted workflows and self-service enablement

Development and enhancement of the MSCI Index Development Platform and the IndexMetrics Real-Time application using modern software engineering practices, Python, Azure, and AI-assisted development tools...

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Build-from-zero applications
• Bridge machine learning and product
• AI-accelerated development
• Real-time visualization
• End-to-end delivery of early-stage applications

• Writing reusable, testable, and efficient code mainly in Python
• Mastering the integration of user-facing elements developed by front-end developers with server side logic
• Integration of data storage solutions
• Handling security and data protection professionally and precisely

• Ownership of on-device inference
• Design and implementation of Android inference solutions
• Development and maintenance of native Android vision layer using C++ and Android NDK
• Creation of JNI bindings connecting native components to Kotlin application

• Technical management of a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients
• Development of highly scalable services realizing AI/ML functionalities

• Writing reusable, testable, and efficient code mainly in Python
• Delivering great user experience by the development of great user interfaces
• Handling security and data protection professionally and precisely
• Collaborative work to provide technical solutions and implement new software features

• Partnering with engineering teams to automate and optimize service availability, scalability, performance, monitoring, and alerting
• Developing software to automate the toil required to keep SaaS offerings highly available, scalable, and resilient

Shaping AWS architectures and technical decisions for web applications, SaaS platforms and enterprise environments...

• Design, develop, and maintain data ingestion and processing pipelines across different data platforms
• Develop and optimize batch and streaming data pipelines using Python, SQL, and distributed processing technologies

• Implementation of end-to-end analytics and dynamic visualizations in Tableau, ThoughtSpot, SAP Analytics Cloud, or equivalent enterprise BI platforms
• Preparation, transformation, and modeling of data using SQL, Python, or dbt leveraging enterprise cloud environments like Snowflake and Microsoft Azure

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management (CSPM) and Cloud-Native Application Protection Platform (CNAPP) tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines

• 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

• 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