Python programmer állás, munka

• 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

• 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, 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

Design, deploy, and maintain observability platforms and tools such as Prometheus, Grafana LGTM+ stack, OTel and others...

• Design and implement data fabric architecture across heterogeneous data sources and platforms
• Develop scalable ETL/ELT pipelines for batch and real-time data integration
• Integrate structured and unstructured data from databases, APIs, SaaS applications, data lakes, and enterprise systems

• Client-facing communication
• Participation in pre-sales discussions
• Collaboration with sales and customer success teams
• Communication with clients' technical teams
• Solution design and delivery
• Design and configuration of data labeling and processing pipelines using Python-based tools

• Leading engagements to build and productionize tailored AI applications in the Databricks platform
• Owning the technical delivery of AI solutions independently or as Delivery Technical Lead
• Leading technical scoping and designing high-level architectures for AI and Generative AI solutions

• 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 and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling by defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• Design and implement automated test cases for backend, frontend, or API-based systems
• Collaborate with distributed teams across multiple time zones
• Participate in requirement analysis to ensure testability and clarity

• Ownership of data models and storage architecture behind AI systems across relational, graph, and retrieval-oriented data
• Building and operating APIs, backend services, and pipelines to keep data current and accessible

• End-to-end AI solution ownership
• Partnership with product managers and business stakeholders for problem translation
• Independent planning and execution of research and experimentation
• Design of AI solutions with system-level perspective

• Working with cutting edge technology enabling the Cloud Journey
• Managing Infrastructure as Code for parts of the Core Platform Layer including Kubernetes Clusters, Message Broker backends, Infrastructure tooling

• 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

• Working with cutting edge technology enabling Cloud Journey
• Managing Infrastructure as Code for parts of Core Platform Layer including Kubernetes Clusters, Message Broker backends, Infrastructure tooling

• Delivery of software releases on schedule with defined scope and quality
• Transition to continuous delivery
• Ensuring reliable operation of CI/CD pipelines and improving their efficiency
• Contribution to test strategy and coordination of its execution
• Development of test frameworks and automation tools

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• CI/CD-folyamatok tervezése, felépítése és üzemeltetése (Azure DevOps, GitLab CI, GitHub Actions vagy Jenkins)
• Infrastruktúra kezelése kód formájában (Terraform, Bicep/ARM, Ansible)
• Konténeres környezetek felépítése és üzemeltetése (Docker, Kubernetes / AKS)

• 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