Hiring.Camp

System Architect

Ericsson

·

Jun 22, 2026

Location
Budapest, HU
Type
Full-time
Department
Engineering
Experience
10+ years
Education
Master
Source
Eightfold

Description

## Join our Team

About this opportunity:

In the Technology and Production Evolution within the Engineering Services and Experience in BNEW we have fully responsibility of being the design owner of the Engineering Environment for Ericsson Networks, the responsibility includes strategy definition and execution, Architecture Runway (ARW) evolvement, governance of architecture and portfolio including security, Test Strategy definition and Technology supply strategy

We are currently seeking dedicated and results-oriented individuals to join The Technology and production Evolution team as a System Architect. The System Architect will assume a critical role in analyzing, creating, and implementing technically feasible solutions that align with our BNEW’s business needs, Architectural standards and forward leaning directions, mainly Scaling AI and embracing Platform engineering discipline and practices.

What you will do:

Platform Engineering

  • Architect and optimize Golden path initiatives with relation to the overall IDP vision and plan, collaboratively with different PEUs, and adhering to the existing system principles, design rules and security requirements.
  • Lead architectural studies, reviews, provide recommendations, while keeping scalability, and operational efficiency in mind of platform components.

AI & Machine Learning Integration

  • Design architectures enabling scalable AI/ML model deployment, monitoring, and lifecycle management (MLOps / LLMOps).
  • Collaborate with data scientists and engineering teams to integrate AI-driven capabilities into products and services within the production environment.

Data Architecture & Management

  • Develop data architecture frameworks covering data ingestion, integration, storage, governance, and security.
  • Support the implementation of data lakes, data warehouses, and real-time streaming pipelines.

Cross‑Functional Collaboration

  • Work closely with colleagues from different PEUs, Portfolio team, Security and operations, to translate business requirements into technical solutions.

Technology Strategy & Innovation

  • Monitor technology trends in AI, platform engineering, and data ecosystems; drive innovation and continuous improvement.
  • Lead proof-of-concept initiatives to evaluate new architectures, tools, and methodologies.

The skills you bring:

  • Degree in Engineering or equivalent experience.
  • Must have 10+ year of experience in SW industry preferably in Telecom domain with good customer handling, technical capabilities.
  • Experience with AI/ML pipelines and MLOps practices.
  • Solid understanding of data management frameworks, data modeling, and distributed data systems.
  • Solid understanding of platform engineering practices, IDP, and Golden path designing.
  • Strong expertise in cloud platforms and cloud-native architectures.

Why join Ericsson?At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply?Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: Hungary (HU) || Budapest

Req ID: 785915

Skills

Machine Learning

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