About this role
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 dependenciesBuilding AI agents that interact with source code, Git platforms, CI/CD systems and engineering toolsDiagnosing build and deployment failures using source code, configuration and pipeline logsCreating RAG-based solutions for searching and understanding large codebases and technical documentationAutomating the generation of architecture and engineering documentationConnecting LLMs to engineering systems through APIs and tool callingBuilding controlled agentic workflows with validation, retries, confidence checks and human approval stepsEvaluating different LLMs and approaches for software engineering use casesTurning successful prototypes into reusable, maintainable and production-ready engineering solutionsYou 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 environmentsStrong programming and automation skills, preferably in PythonHands-on experience with Kubernetes, containers and cloud-native architecturesGood understanding of Git-based development workflows and CI/CD systems such as GitLab CI, Jenkins, GitHub Actions or similarExperience integrating systems through REST APIs, SDKs and automationStrong understanding of how modern distributed applications and cloud services are built, deployed and operatedHands-on experience building or integrating Generative AI / LLM-based applicationsAI 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 searchLLM tool calling and integration with external systemsAI agents and multi-step agentic workflowsAgent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents SDK or comparable technologiesLLM evaluation, observability and quality validationAI-assisted source-code analysis or developer toolingExperience 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:
OpenStack or other large-scale cloud platformsInfrastructure as Code such as Terraform or AnsibleGitOps and platform engineering practicesVector databases such as pgvector, Qdrant, Weaviate, Milvus or similarLlamaIndex or comparable retrieval/indexing technologiesDeveloper productivity or internal developer platform toolingWorking with large or unfamiliar software repositoriesSoftware architecture analysis and service decompositionTechnical due diligence, software transition or engineering handover projectsOperating software in security- or sovereignty-sensitive environments
* Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.