Python programmer állások Budapesten (it programozás, fejlesztés)

Architect, build, and operate scalable inference services, APIs, and backend components for model-driven and LLM-powered product features...

• Design and build knowledge tracing and longitudinal learner models that support mastery and progression features surfaced to customers
• Define what "mastery" and "progression" mean operationally: constructs, model targets, and evaluation criteria aligned to learning outcomes and product requirements

• Build and maintain scalable cloud infrastructures using modern services and best practices
• Proactive detection, analysis, and fixing of vulnerabilities and performance bottlenecks in cloud environments

• Build and operate multi-cluster containerized environments across cloud platforms
• Create end-to-end CI/CD pipelines for cloud-native applications and machine learning workloads
• Lead cost-optimization and FinOps initiatives for GPU, storage and data-egress spend

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

• Design and ship production-grade agentic systems
• Take technical ownership of Compliance in a Box (CiaB) AI platform
• Architect and evolve AI reasoning layer and agent orchestration framework
• Build and maintain the GRC Brain (AI reasoning layer) and Agentic Cortex (agent orchestration framework)

• Building transformative AI capabilities from the ground up
• Developing user-facing features with conversational and agentic AI technologies
• Experimenting with generative AI models and techniques
• Tackling challenges at the intersection of AI and enterprise software

• End-to-end implementation of data pipelines and GenAI applications on Databricks
• Design and maintenance of CI/CD, model serving, monitoring, and evaluation frameworks for AI production reliability
• Tuning of complex Spark jobs and LLM inference patterns for efficiency and latency optimization

• Architect and implement scalable AI/ML solutions on Azure or GCP
• Design and build AI solutions for sales use cases such as automated RFP/RFI ingestion, classification, and response generation
• Design and build AI solutions for intelligent document understanding

• Design, develop, and optimize scalable data pipelines for processing large-scale structured and unstructured data
• Ensure reliability, performance, and scalability of data systems
• Build solutions to handle real-time streaming data from multiple sources

• Leading end-to-end penetration testing engagements across web applications, APIs, cloud environments, Kubernetes, AWS, Azure, GCP, and internal infrastructure
• Simulating real-world attack scenarios to identify security risks before they become threats

• Development of highly scalable services realizing AI/ML functionalities
• Implementation of machine learning solutions addressing computer vision and NLP use cases
• Work with Large Language Models (LLMs) and perform prompt engineering tasks

• Monitoring configuration, maintenance and usage of environments with virtual machines and cloud based infrastructures (Kubernetes)
• Cooperation with platform engineers, software architects, delivery teams and operational units

• 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

• 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

• Definition and development of system-level tests for high-end embedded computing systems
• Automation of test execution and evaluation through scripting in a CI environment
• Increase of test coverage by extending functional examples into comprehensive test suites

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling