Software development engineer állás, munka

• 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

• Designing and developing backend services and APIs that support real-time data ingestion, transformation, and delivery
• Building scalable, low-latency, high-throughput systems capable of handling demanding performance requirements

• 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

• Writing production-quality, well-tested scraper code in Python
• Learning the architecture of the collection platform including scrapers, proxy layer, queues and enrichment API
• Debugging issues across multiple components using logging, monitoring and alerting tools

• Writing and maintaining clean, high-quality, and efficient code that aligns with project specifications using AI tools daily to accelerate delivery
• Executing comprehensive testing and seamless deployment of programs and 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

• Design and develop distributed backend services using .NET and event-driven architecture
• Collaborate with product and commercial teams to turn engagement ideas into scalable, maintainable, and user-friendly solutions
• Optimize microservice ecosystem for performance, reliability, and observability

• 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

• Managing complex database systems within a global technology ecosystem
• Building and managing database hosting capabilities using Infrastructure as Code for all platform needs
• Building database infrastructure platforms with developers and providing guidance and tooling to handle distributed systems

• Understanding new Arm architecture and IP features and their implementation
• Devising promotion of benefits to software ecosystems and resolving adoption issues
• Analysis and deep understanding of software solutions deployed in products, particularly major open-source projects such as Linux

• Setting up and maintaining databases for payment systems
• Maintaining, monitoring and supporting production and development of databases and environments
• Monitoring and troubleshooting database integrity, performance, blocking and deadlock, replication/log shipping, connectivity, and security issues

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• Tackling complex technical challenges focused on scalability, reliability, and speed
• Maintaining a high-performance infrastructure that processes millions of events with low latency
• Collaborating with Data Scientists and Engineers to design and implement cutting-edge feature topologies

• 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

• Preparation for containerization
• Increasing scalability of systems
• Maintaining high quality of systems
• Re-evaluating systems design
• Developing new features
• Building a scalable, multi-brand gaming platform

• Real-time decision making with machine learning for risk assessments
• Ownership of risk infrastructure for Instant ACH in the US market
• Cross-functional collaboration with software engineers, data analysts, and data scientists

• 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

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