vultr - Senior AI Platform Engineer, Core Cloud Engineering
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Requirements
• Hands-on experience deploying and operating LLM inference systems — vLLM, SGLang, TGI, or comparable — at non-trivial scale. • Strong Docker and container skills; comfortable owning the full container lifecycle from image build to production. • Deep familiarity with GitLab CI/CD — pipeline authoring, custom runners, artifact management, and integrating external tooling. • Working knowledge of MCP or similar context-injection patterns for grounding LLMs against private or internal data. • Demonstrated ability to evaluate open-source models for specific task fit — not just benchmarks, but real use-case performance against internal workloads. • Strong software engineering fundamentals — this role writes real code, not just configuration. • Experience with RAG pipelines — vector databases, chunking strategies, retrieval evaluation — especially over code or technical documentation. • GPU infrastructure familiarity — CUDA basics, multi-GPU serving, memory management under inference load. • Ability to communicate technical tradeoffs clearly to engineers, managers, and leadership; track record of moving organizations toward new practices.
Responsibilities
• Evaluate and curate open-source models — Llama, Mistral, Qwen, DeepSeek, Kimi, and others — for fit across engineering use cases including code generation, review, test writing, and summarization. • Build and maintain MCP (Model Context Protocol) servers that expose internal context — codebases, runbooks, incident history, architecture docs, development environments, and testing suites — to AI assistants and coding agents. • Integrate AI capabilities directly into GitLab CI/CD pipelines: automated code review, test generation, changelog drafting, PR summarization, and anomaly detection in build output. • Own the model lifecycle: versioning, A/B routing, quantization tradeoffs, and performance benchmarking under real engineering workloads. • Drive AI adoption across the software engineering organization — identify high-leverage workflows, instrument usage, and iterate based on real data on time-savings and quality impact. • Build and configure IDE tooling integrations — Cursor, Continue, and Copilot alternatives — backed by internal inference endpoints, keeping code off third-party APIs wherever possible. • Produce documentation, internal workshops, and working examples that help engineers go from AI-curious to AI-reliant — including a shared library of prompts, system instructions, and RAG pipelines tuned for Vultr’s stack. • Collaborate closely with Software Engineers, SREs, and Network Engineers to ensure the AI platform layer serves all teams without becoming a bottleneck or single point of failure.
Benefits
• $110,000 - $140,000 • Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws. • INCLUSION & PRIVACY
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