E learning állások VII. kerületben (it programozás, fejlesztés)

• Design, implement, and optimize machine learning models to solve complex business problems
• Oversee the entire machine learning lifecycle from data collection and pre-processing to model deployment and monitoring

• Convey knowledge of the Instructure learning platform to clients and serve as an expert for Instructure products
• Coordinate client projects on key initiatives and provide best practices and guidance to achieve the desired results of those key initiatives

• 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

• Development, adaptation, and deployment of end-to-end tracking solutions for sports officiating and analytics
• Implementation and optimization of ML models using Python, C++, PyTorch, NumPy, Pandas, and OpenCV
• Design and building of ETL pipelines and data manipulation workflows

• üzleti és technikai adatforrásokból származó adatok összegyűjtése, tisztítása és strukturálása
• adatmodellek kialakítása (DWH szemlélettel), adatstruktúrák felépítése
• komplex SQL lekérdezések és adattranszformációk készítése, teljesítményoptimalizálása

• Build and operate deployment pipelines for AI services covering CI/CD, infrastructure-as-code, environment promotion, and rollback
• Deploy and operate model serving, batch scoring, and orchestration pipelines across development, staging, and production

• 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

WHAT YOU CAN EXPECT IN THIS ROLE (IN SENTENCES)   You will support the daily administrative and operational tasks connected to MOL’s fuel export–import processes. You will cooperate with the Group Fuels Trading team and other internal stakeholders while learning the fundamentals of international...

• Design and implementation of the server architecture of the flagship product iREMS
• Finding solutions and developing to production stage for complex problems
• Collaboration with teams located in Budapest and Oradea to ensure constructive communication with co-developers and consulting/support teams

• Management of contract initiation, negotiation, execution, acceptance, and maintenance
• Oversight of contract negotiations and approvals
• Maintenance of contract templates and standard agreements
• Collaboration with senior management on contract strategies

• 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

• 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

• Project requirements gathering
• Project resource responsibility assignment
• Project charter documentation creation
• Project schedule preparation and resource assignment
• Project risk identification and mitigation planning
• Communication plan crafting and coordination

• Design and development of interactive 3D, XR, and digital twin applications
• Creation of immersive visualization experiences for industrial, engineering, facility, and enterprise use cases

• Designing, developing, and improving ball and player tracking algorithms using multi-camera systems
• Owning and evolving multi-view geometry solutions for accurate, court-wide tracking
• Developing robust approaches to occlusion handling, recovery, and failure modes in fast-paced sports scenarios

• Building skills in product management through a tailored learning program
• Executing on real problems in product management
• Working on own projects/products during the 12 week tailored programme
• Spending time on dedicated sessions to learn about product management
• Working on domain team projects

• 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

• 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, build and deploy LLM-powered pipelines and autonomous agents
• Scope and deliver AI solutions with stakeholders
• Set best practices around MLOps and production-grade AI
• Mentor developers and contribute to internal knowledge-sharing

• Turning data science and AI experiments into production-ready, scalable solutions
• Developing and deploying Generative AI solutions including LLMs and Diffusion Models
• Designing and implementing data models and flows including ETL, embedding models and vector databases