Machine learning állások Budapesten (it programozás, fejlesztés)

• Design of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns
• Development of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns

• Shaping AWS-based cloud architectures and technical decisions for SaaS, PaaS, web application, and enterprise cloud environments
• Design and implementation of Infrastructure as Code solutions using tools such as Terraform, AWS CloudFormation, or Pulumi

• 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...

• 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

• 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

• 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

• 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.

• Leading and mentoring a team of ML engineers
• Owning model quality end-to-end from experimentation to production-ready artifact
• Conducting regular 1-on-1s for career development and feedback
• Removing blockers and ensuring team well-being

• Build and maintain production-grade AI infrastructure using Infrastructure as Code (Terraform/CDK), monitoring, logging, and deployment automation
• Optimise the performance and cost efficiency of LLM workloads through autoscaling, caching, batching, and infrastructure right-sizing strategies