Python developer állások Budapesten középiskolai végzettséggel

• Leading and mentoring a team of ML engineers
• Owning model quality end-to-end from experimentation to production-ready artifact
• Conducting regular 1-on-1s for career development and feedback
• Removing blockers and ensuring team well-being

• Experience in different Data Science topics
• Independent delivery of Data Science projects with product-oriented mindset
• Engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Architect Zero-Trust environments for LLM-based solutions
• Implement private networking (Private Link), granular RBAC (Entra ID), and automated policy enforcement
• Ensure compliance with enterprise security and audit standards
• Bridge Data Science and Engineering

Work directly with clients, product owners, architects, and development teams to understand business and technical requirements and translate them into infrastructure and cloud architecture requirements...

• Building and maintaining dataset ingestion, cleaning, and versioning pipelines
• Designing and operating labeling workflows including quality control and inter-annotator agreement tracking
• Owning dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management and Cloud-Native Application Protection Platform tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines including IaC scanning and policy-as-code

• Skálázható cloud-native adatarchitektúrák tervezése Databricks vagy MS Fabric platformokra
• Adatmodellek, sémák és tárolási minták kialakítása (lakehouse, Delta, Parquet)
• Batch és streaming adatok betöltési pipeline-jainak megtervezése
• ETL/ELT folyamatok és transzformációs stratégia meghatározása

Agent workflows with LangGraph / LangChain / PydanticAI / CrewAI (routing, retries, fallbacks, timeouts, human-in-the-loop)....

• Building AI-powered automation for complex cloud engineering environments
• Combining platform engineering, software development and cloud expertise with modern Generative AI technologies to automate parts of the software development lifecycle

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, kubernets, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks before they become threats

• 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

• Coordination of operations together 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

• 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

• Administration and support of enterprise DNS, DHCP and IPAM services using CygnaLabs QIP
• Management of DNS zones and DNS records (A, AAAA, CNAME, PTR, MX, TXT, SRV)
• Configuration and maintenance of DHCP scopes, reservations, exclusions, options and lease management

• Perform web, network, and infrastructure penetration tests independently.
• Actively participate in red team exercises (e.g., initial access, lateral movement, persistence).
• Document test results and deliver detailed, client-friendly reports.

• 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

• 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

• On-premise IT infrastruktúra üzemeltetése, támogatása és folyamatos fejlesztése
• Linux alapú rendszerek adminisztrációja
• Kubernetes klaszterek adminisztrációja
• Middleware komponensek adminisztrációja
• Automatizációs feladatok megvalósítása és karbantartása

• 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