Python állások IV. kerületben (ai és automatizáció)

• 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).

• 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, develop, deploy, monitor, scale, and troubleshoot ML and LLM-based systems that power shared AI services across the platform
• Build secure, high performance microservices and platform components on AWS, owning services end-to-end from implementation through deployment and production monitoring

• Build and maintain simulation environments for mobile robotic perception and navigation
• Develop and integrate robotic behaviours and learning pipelines for reinforcement learning and motion planning in complex outdoor environments

• 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

• 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

Performing tasks related to the position....

• Understanding underlying data structure of an ERP system (Microsoft Dynamics 365 Finance & Operations)
• Creating technical specification (design) documents for functional requirements
• Managing existing and creating new automations using Alteryx, Python, SSIS
• Creating reports and dashboards in Power BI

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Automation and streamlining of fraud investigation processes
• Improvement of tooling for fraud investigations
• Creation of seamless experience for agents and customers during fraud investigations
• Development of innovative solutions to tackle fraud

• Creation and preparation of 3D assets, environments, and scene layouts for Omniverse and OpenUSD workflows
• Assembly of digital-twin scenes for industrial, facility, product, or enterprise visualization use cases

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize artificial intelligence and machine learning solutions
• Assess client needs and data for AI/ML solutions

• 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 Terraform, AWS CloudFormation, or Pulumi

• Design, develop, and maintain C-based applications in Linux/UNIX environments
• Work with system-level interfaces (POSIX APIs, processes, file systems, sockets)
• Develop and enhance Kerberos authentication systems and related tooling

• Research, develop, implement, and test artificial intelligence algorithms to improve the quality and robustness of the static world model in automated vehicles
• Focus on road structure including lanes, curbs, and drivable trajectories

• Development, governance, deployment, and ongoing enhancement of liquidity analytics models and supporting infrastructure across asset classes
• Driving the analytics product lifecycle, including prioritizing initiatives, influencing roadmap decisions, and ensuring successful deployment into production

• Development of the Tulip OS
• Maintenance and support of bootloader, kernel, and userspace development for Tulip hardware
• Working alongside product management and customers to identify key features and product performance
• Maintaining the Tulip Operating System including building, packaging, and support

• Development of AI and generative AI services
• Building multi-agent AI systems using modern LLM frameworks
• Design of scalable backend services in Python
• Containerization and deployment using Docker and Kubernetes
• Building CI/CD and MLOps pipelines for automated AI delivery