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AI Engineering Workflow Developer for T Cloud Public (m/f/d)

Deutsche Telekom IT Solutions Slovakia
CompanyDeutsche Telekom IT Solutions Slovakia
CategoryEngineering
LocationKošice
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted11 Aug 2026
Last verified12 Aug 2026
SourceThe employer's own careers page (company_site)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Mission Support Meridian engineering teams by building, testing, and maintaining AI-assisted development workflows that accelerate code understanding, documentation, build triage, and handover preparation for cloud software and platform work packages. Role focus This more junior position supports senior AI SWE and platform specialists by implementing workflow utilities, prompt packs, repository analysis scripts, documentation helpers, and integration adapters. The candidate should be eager to learn enterprise cloud platforms, AI development tooling, and disciplined software handover practices. Key responsibilities • Implement and maintain small AI-assisted engineering utilities for repository indexing, code summarization, dependency extraction, log parsing, and documentation generation. • Support senior engineers in configuring AI development environments, testing prompts, comparing model outputs, and documenting repeatable SDLC usage patterns. • Create scripts and lightweight services that connect Git repositories, CI/CD logs, issue trackers, documentation stores, and internal model endpoints. • Help prepare handover materials including codebase summaries, service notes, build observations, glossary entries, and structured evidence templates. • Test open-source, open-weight, and Chinese coding models in approved environments and document strengths, limitations, risks, and practical usage guidance. • Participate in reviews with senior engineers to validate AI-generated outputs, correct inaccuracies, and improve workflow quality over time. Examples of market tools, models, and SDLC platforms expected • AI development environments such as Cursor, Windsurf, Continue, Cline, Aider, Claude Code, or VS Code-based extensions configured for enterprise repositories. • Model families used for coding support such as DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or other internally approved models. • Workflow and integration tooling such as Python, FastAPI, notebooks, LangChain, LlamaIndex, GitLab/GitHub APIs, Jenkins APIs, Markdown, and documentation automation. • Supporting engineering tools such as Git, Docker, Kubernetes basics, Helm basics, Linux shells, package managers, log processing, and structured prompt repositories.