Python programmer állások V. kerületben (ai és automatizáció)

• Setting technical direction across teams
• Leading design of secure, scalable MCP hosts and AWS microservices
• Influencing AI adoption through AI first solution design, model guidance, prompt strategy, and built-in governance and monitoring

Development and enhancement of quantitative financial models Application of advanced technologies to real world financial problems Keeping up to date with the latest technological trends ...

• 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

• AI-alapú rendszerek és alkalmazások architektúrájának tervezése és fejlesztése
• Microservice és event-driven architektúrák tervezése és technikai irányvonal meghatározása
• Architekturális döntések meghozatala, beleértve a megfelelő technológiai és tervezési megoldások kiválasztását

• Magas szintű technikai és üzleti tanácsadás ügyfelek számára az Atlassian termékekkel és saját fejlesztésű megoldásokkal kapcsolatban
• Ügyfelek üzleti és IT folyamatainak felmérése
• Komplex megoldások tervezése, bevezetése és minőségbiztosítása

• Architect and implement cloud-native data solutions using GCP (BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Cloud Storage)
• Lead the design and development of complex data pipelines and distributed processing frameworks that ensure scalability, performance, and operational reliability

• Building and maintaining simulation environments for mobile robotic perception and navigation
• Developing and integrating robotic behaviours and learning pipelines for reinforcement learning and motion planning in complex outdoor environments

• ETL Framework Development
• Pipeline Optimization
• Modern Engineering Practices Application
• Infrastructure & Automation Management

Research, development, implementation, and testing of artificial intelligence algorithms to improve the quality and robustness of the static world model in automated vehicles...

• Design, reliability, and operational excellence ownership of cloud and hybrid infrastructure
• Definition, tracking, and improvement of service reliability using SLO/SLI frameworks
• Driving availability, latency, and scalability targets across platforms

• 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

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

• 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

• Configuration and evolution of enterprise AI platforms and orchestration components
• Implementation of platform capabilities and contribution to AI use case implementation
• Development of APIs, service components, and integration logic for AI-enabled solutions

Mastering Apache Spark and Databricks on AWS/Azure Delivering hands-on project work during bi-weekly sprint model Completing complex capstone projects Shadowing senior consultants on real-life engagements Delivering simulated projects Advising stakeholders and navigating requirements Contributing...

• End-to-end AI solution ownership from problem definition through prototype validation and production support
• Partnership with product managers and business stakeholders to translate real-world problems into data science and AI initiatives

• End-to-end software release process management including branch management, software version control, release note documentation, and communication with internal and external collaborators
• Release automation enhancement by improving CI/CD pipelines and defect logging and tracking for releases

Build and maintain data models and pipelines primarily using AI coding tools with consistent best-practice patterns, clear documentation, and high-quality standards...

• Translation of AI opportunities into executable technical solution designs
• Definition of integration approaches with IT and business application/data owners
• Implementation of AI use cases using enterprise AI services and LLM APIs

• CI/CD pipeline-ok kialakítása, fejlesztése és karbantartása
• Deployment és release folyamatok automatizálása
• Cloud infrastruktúrák üzemeltetése és optimalizálása
• Konténerizált környezetek kezelése (Docker, Kubernetes/OpenShift)
• Monitoring, logging és alerting rendszerek kialakítása és üzemeltetése