Python programmer állások XI. kerületben (ai és automatizáció)

• 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

• 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

• Setting the technical direction for AI-enabled platform
• Architecting secure, scalable, serverless systems on AWS
• Defining patterns and governance practices for multiple teams
• Guiding AI value addition across the stack
• Mentoring senior engineers
• Leading org-wide design decisions

• Design and build agentic systems that plan, call tools and automate real business workflows
• Develop LLM-powered applications including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration

• 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).

• Kapcsolattartás az ügyfelekkel, igények felmérése és tanácsadás
• Azure Function App fejlesztés
• Dokumentálás
• Programozás Pythonban
• Azure infrastruktúra tervezés és karbantartás
• Rendszeres ellenőrzés, frissítés és teljesítmény optimalizálás Azure környezetben
• Fejlesztések tervezésében aktív részvétel

• Build and maintain the GRC Brain (AI reasoning layer) and Agentic Cortex (agent orchestration framework)
• Design and implement a 7-agent architecture: OnboardingAgent, ScopeAgent, StandardsMapper, RiskQuantifier, PolicyAnalyzer, InfrastructureAuditor, and ComplianceMonitor

• Design, development, and optimization of core ML models, especially in Computer Vision and Deep Learning
• Development of efficient, scalable training and inference pipelines
• Mentoring junior ML engineers, reviewing code and sharing best practices

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

• Support the design, implementation, and ongoing management of portfolio solutions for clients
• Keep up to date with client trends, industry news, events, research, and regulations
• Perform investment analysis and provide insights across all stages of the investment process

• Leading a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Providing mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients

• The CloudOps Engineer is responsible for availability for cloud platforms
• Respond, troubleshoot and resolve production alarms
• Communicate and troubleshoot operational issues supporting a complex environment
• Respond to cloud service outages and manage incident tickets

• 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

• 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

Performing tasks related to the position....

• 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

• Development, governance, deployment, and ongoing enhancement of liquidity analytics models and supporting infrastructure across asset classes
• Driving the analytics product lifecycle with cross-team partnership to prioritize initiatives and influence roadmap decisions

• Ownership of backend services throughout their lifecycle including design, implementation, testing, deployment, operation, and continuous improvement
• Translation of product and business requirements into maintainable technical designs with clear communication of risks and trade-offs

• Building AI-assisted development workflows for code understanding, documentation, build triage, and handover preparation
• Implementing and maintaining AI-assisted engineering utilities for repository indexing, code summarization, dependency extraction, log parsing, and documentation generation