Endless Lifecycle Support (ELS) enables organizations to continue securely using Linux distributions and software languages that have reached the end of life or no longer receive standard security support – delivering vulnerability patches for unsupported versions of CentOS, Ubuntu, Debian, Oracle Linux, PHP, Python, Spring frameworks, Angular/AngularJS, Django, Flask. For more information, visit our website: [tuxcare.com/endless-lifecycle-support](https://tuxcare.com/endless-lifecycle-support)
Requirements:
To be successful, you should have:
Education & Experience
BSc or MSc in Computer Science, Engineering, or a related field.
5+ years in software engineering as a developer, tech lead, or team lead.
Demonstrated ownership of complex systems or components (not just small features).
AI & LLMs
Hands-on experience integrating LLMs in real projects (OpenAI, Anthropic, etc.).
Familiarity with common approaches such as RAG, CAG, agents, tool-calling, etc.
Software Engineering
Strong Python skills, including:
Asyncio / multithreading / multiprocessing concepts and usage.
Testing frameworks (pytest, unittest) and a solid testing culture.
Experience with database design and querying:
PostgreSQL or another SQL database.
MongoDB or another NoSQL database.
Infrastructure & Tooling
Experience with CI/CD tooling (Jenkins, GitLab CI, or similar), test automation, and containerization (Docker).
Confident Linux CLI user.
Experience supporting or operating services on Linux/Unix VMs.
Willingness and desire to use AI development tools (e.g., Cursor) in day-to-day work.
Ways of Working
Ability to take loosely defined problem statements and turn them into concrete plans (architecture, milestones, experiment design) with minimal supervision.
Strong written communication skills in English (design docs, experiment logs, clear merge requests).
It is a plus if you also have:
Published research or documented experimentation experience in any Computer Science topic.
Experience formulating and testing hypotheses (e.g., comparing models, algorithms, or system designs).
Data analysis skills:
Representing results via tables, plots, or dashboards.
Designing meaningful metrics for system performance and quality.
Basic knowledge of the C language (able to read patches, understand diffs and build issues).
Regular usage of AI engineering tools (Cursor, WindSurf, aider, Claude Code, etc.).
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