Python programozó állások, munkák Dunakesziben

• Writing reusable, testable, and efficient code mainly in Python
• Mastering the integration of user-facing elements developed by front-end developers with server side logic
• Integration of data storage solutions
• Handling security and data protection professionally and precisely

• Design and build agentic systems that plan, call tools and automate real business workflows
• Develop LLM-powered applications including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration

• 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

• Kutatás-fejlesztési projektek végigkövetése a tervezéstől az átadásig
• Beágyazott vezérlők programozása C nyelven
• Prototípus berendezések beüzemelése
• Prototípus berendezések tesztelése
• Belsős és partner cégek villamos mérnökeivel való együttműködés

• 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

• Maintaining and evolving data structures
• Building new ETL flows and maintaining/improving existing ones
• Designing new data structures and data modelling concepts
• Ensuring data quality by verifying data consistency and accuracy
• Creating data validation methods

• Technical management of a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients

• P&P platform configuration end to end across frontend and backend
• Forecast and attribution model tuning and optimization
• Custom business logic writing in SQL, Python, or Go
• Promotional campaign evaluation and analytical reporting
• Structured problem-solving on complex retail configuration challenges

• Leading architecture design
• Driving technology decisions
• Co-creating innovative products
• Mentoring clients to adopt Agile practices and engineering excellence
• Leading project workstreams
• Building scalable solutions using Agile methodologies
• Co-creating products with client teams

• Design and build innovative AI solutions combining advanced software development and data engineering
• Collaborate with global technologists, data scientists, and industry experts to transform businesses through advanced analytics and AI-driven solutions

• Collaboration with a global network of technologists, designers, and strategists
• Development of cutting-edge products
• Mentoring client teams
• Shaping engineering cultures
• Development of software products and services from the ground up

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

• Producing high quality code for the full technological stack from database to UI
• Assisting with troubleshooting basic issues
• Improving application performance by responding to specific problems and fixing defects
• Documenting and demonstrating solutions with a focus on security and performance

• Design process of a SoC by integrating the latest Arm IP products
• Development of RTL code using System Verilog
• Running EDA tools to check the design and analyze results
• Using AI-assisted engineering tools to support development and debug
• Identifying and fixing bugs

• Delivery of agentic solutions across multiple client deployments
• Building and configuring full-stack AI-enabled solutions
• Embedding with client service teams and client teams to understand needs
• Translating client needs into technical and functional requirements

• Working across the software stack including firmware, operating systems, device drivers, compilers, runtimes and middleware
• Contributing to existing projects while exploring and prototyping ideas for the future
• Gaining hands-on experience across the software development lifecycle

• Building, operating and improving production data pipelines using Python, Airflow, Kubernetes and related technologies
• Designing scalable data pipelines and data models for growing data volumes and processing needs

• Development and refinement of core sports betting models for predicting match outcomes
• Building and maintenance of player statistics models analyzing player performance
• Design and implementation of risk management models to optimize exposure

• 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

• 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