Machine learning állások, munkák Budapesten

• Design and code clean, maintainable and high-performance custom-built solutions based on Java technologies and related frameworks
• Follow defined methodology and standards to create software of high quality
• Conduct code reviews and provide constructive feedback to team members

Identification and approach of potential B2B clients in Hungary, EMEA, and the USA with focus on technology, digital transformation, cloud, and web development opportunities...

• A gyakorlati foglalkozások feltételeinek biztosítása: anyagok, kellékek, szabványok és segédanyagok beszerzése, kiszabása, a vagyonbizottság biztosítása
• A Gyártástechnológia tárgyhoz kapcsolódó gyakorlatok előkészítése, üzemi gyakorlatok szervezése, ellenőrzése

• 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

• Generate new business opportunities and secure tenants for the clients industrial and logistics portfolio in Hungary
• Manage the leasing process from prospecting to contract negotiation and deal closure

• Working on the next generation vehicle's self-driving software
• Development of the internal data distribution and execution framework
• Integration of third-party frameworks
• Sensor data recording related tasks
• Development of human-machine interfaces and data visualization tools

• Use formal verification algorithms to formally prove the correctness of complicated logic problems
• Identify key behaviors for verification to write clear testplans for complex designs
• Implement testplans using innovative formal techniques
• Drive tools to realize their best performance

• Mentorship and coaching of up to 10 recruiters
• Performance management including regular reviews, one-on-ones, IDPs, and team goal setting
• Process optimization of all recruitment stages
• Innovation and tools leadership for testing and implementing new technologies

• Project management and execution of every stage of the project lifecycle
• Project planning and coordination including detailed project plans, timelines, delivery schedules, risk assessments, and mitigation strategies
• Resource management to meet project deliverables and optimize project profitability

• Technical assistance and training to customers globally
• Mentoring partners on IP integration, configuration, and performance
• Investigating issues and finding solutions through RTL simulations
• Communicating technical results clearly
• Analyzing customer requests and issues to provide solutions

• 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

• Implementation of transformation projects across sectors
• Independent management of workstreams with support from Delivery or Engagement Manager
• Collaboration with McKinsey colleagues and client teams
• Leadership of junior consultants and clients in projects or workstreams

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• ServiceNow megoldások tervezése megfelelve üzleti, funkcionális és megfelelőségi követelményeknek
• Szoros együttműködés a Scrum csapattal backlog elemek átdolgozása, követelmények tisztázása és pontos becslések készítése érdekében

• Leading and mentoring a cloud engineering team of 5-7 cloud engineers
• Providing direct line management, coaching, and professional development for a team of cloud engineers and migration specialists
• Fostering a high-performing, technically excellent team focused on secure and scalable migrations

Understanding clients’ mid-term strategies and managing executive relationships with business and IT leaders...

• Training and fine-tuning models against curated datasets
• Building and maintaining evaluation suites
• Analyzing model failures and translating findings into dataset or training improvements
• Packaging models with documentation, benchmarks, and reproducible evaluation results

• 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 including machine learning, LLM, deep learning
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements

• Develop agentic AI applications from refined business and technical requirements.
• Implement agent workflows, prompts, model integrations, memory, RAG, tool calling, security controls, and human-in-the-loop steps.