Senior AI Platform Engineer – T Cloud Public (REF5735F)

Deutschetelekomitsolutions

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Budapest, Szeged, Pécs, Debrecen, , Hungary Full-time Posted today

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We are looking for a Senior AI Platform Engineer to build AI-powered automation for complex cloud engineering environments.

You will combine your platform engineering, software development and cloud expertise with modern Generative AI technologies to automate parts of the software development lifecycle (SDLC).

Your work will focus on building reusable AI-assisted workflows that help engineering teams understand large codebases, discover service dependencies, diagnose CI/CD failures, analyze cloud services and generate reliable technical documentation.

This is not a traditional data science or machine learning role. We are looking for an engineer who understands how modern software and cloud platforms work and can apply LLMs, RAG and AI agents to real engineering problems.

What you'll work on

You will design and build AI-powered engineering workflows such as:

Analyzing large Git repositories and identifying service boundaries and dependencies
Building AI agents that interact with source code, Git platforms, CI/CD systems and engineering tools
Diagnosing build and deployment failures using source code, configuration and pipeline logs
Creating RAG-based solutions for searching and understanding large codebases and technical documentation
Automating the generation of architecture and engineering documentation
Connecting LLMs to engineering systems through APIs and tool calling
Building controlled agentic workflows with validation, retries, confidence checks and human approval steps
Evaluating different LLMs and approaches for software engineering use cases
Turning successful prototypes into reusable, maintainable and production-ready engineering solutions

You will work closely with cloud, platform and software engineers within the T Cloud Public / Meridian engineering environment.

Your core experience

Several years of professional experience in Platform Engineering, DevOps, Site Reliability Engineering, Cloud Engineering or software engineering in cloud-native environments
Strong programming and automation skills, preferably in Python
Hands-on experience with Kubernetes, containers and cloud-native architectures
Good understanding of Git-based development workflows and CI/CD systems such as GitLab CI, Jenkins, GitHub Actions or similar
Experience integrating systems through REST APIs, SDKs and automation
Strong understanding of how modern distributed applications and cloud services are built, deployed and operated
Hands-on experience building or integrating Generative AI / LLM-based applications

AI experience

You do not need to be a machine learning researcher.

We are looking for practical experience applying AI to engineering problems, ideally including some of the following:

RAG (Retrieval-Augmented Generation) and semantic/vector search
LLM tool calling and integration with external systems
AI agents and multi-step agentic workflows
Agent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK or comparable technologies
LLM evaluation, observability and quality validation
AI-assisted source-code analysis or developer tooling

Experience with one framework or model is more important than experience with a specific technology from this list.

Nice to have

Experience in any of the following would be an advantage: