Sql developer állások I. kerületben (ai és automatizáció)

• 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 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

• Design, delivery and evolution of secure, scalable Ruby on Rails applications and services
• Setting technical direction for major projects and platform initiatives from solution design through implementation and production ownership

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize AI and machine learning solutions combining custom models with cloud and third-party AI services
• Assess client needs and data for AI/ML solutions

• Writing and maintaining clean, high-quality, efficient and well tested code aligning with project specifications
• Architecting and implementing key abstractions bridging software engineering and DevOps
• Proactively identifying pain points in the platform and proposing elegant solutions

• Maintenance of the Tulip Operating System including building, packaging, and support
• Configuration and maintenance of routing, networking, and device configurations
• Maintenance and addition of bootloader, kernel patches, and device trees on Tulip hardware
• Porting the Tulip OS to other hardware targets

• Design, develop, and maintain software solutions supporting ultrasound reporting and clinical workflows
• Develop and integrate structured medical reporting solutions using TypeScript, XML, JSON, and SQL databases

• Design and implement enterprise-scale data pipelines and advanced analytics dashboards
• Develop and optimize data ingestion, transformation, and modeling workflows across structured data environments and cloud-based platforms

• Development and operation of data pipelines
• Building data layers in OneDataLake (ODL) Central Data Store Consumer (CDSC)
• Balancing business and IT demands to build modern and efficient pipelines in public cloud environments
• Integration of data preparations into managed processes

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments (Azure, AWS, or GCP)
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• Development and fine-tuning of AI models including LLMs, Agentic AI, and Generative AI systems
• Building and deploying robust AI applications and services on cloud platforms
• Design and implementation of efficient AI pipelines from data preparation to model deployment and monitoring

Automatizálás és digitalizáció manuális hibakeresési, KPI-ellenőrzési és vizsgálati folyamatok kiváltásával okos, automatizált megoldásokkal és scriptekkel...

• Design, develop, and maintain backend services and APIs that support scalable cloud and on-prem systems
• Participation in architectural discussions, prototyping, and evaluating technical feasibility
• Optimization, testing, and continuous improvement of system performance and reliability

• Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses
• Active monitoring and optimization of pipelines for performance and cost
• Configuration of pipeline deployments and management of environment settings using IaC tools and CI/CD

• Lead the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• Ownership of architecture and delivery of production-grade LLM systems and classical ML solutions
• Design, evaluation, and optimization of RAG pipelines including retrieval strategy, chunking, indexing, and monitoring

• 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

• Development, maintenance, and support of cloud-based data operation infrastructure and production data pipelines
• Design, implementation, operation, and optimization of Azure data services and Databricks workloads

• 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

• 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