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 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.
Candidate profile
2-4 years in software engineering, DevOps automation, data engineering, AI tooling, or platform-adjacent development roles.
Good Python skills and willingness to work across APIs, scripting, developer tooling, documentation, tests, and lightweight automation services.
Hands-on familiarity with AI coding assistants, prompt engineering, LLM APIs, local model experimentation, or RAG-style development is strongly preferred.
Basic understanding of Git, CI/CD, Linux, containers, cloud platforms, and software architecture documentation, with readiness to deepen OpenStack knowledge.
Careful working style with good documentation habits, curiosity, and ability to escalate unclear findings instead of over-trusting AI-generated answers.
Comfortable working in a confidential enterprise environment where learning speed, quality discipline, and structured communication are important.
Please note: remote working is only possible from within Hungary due to European taxation regulations.
* Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.