Senior AI Platform Engineer, Core Cloud Engineering
New
V
VultrCloud Infrastructure
Remote - United StatesFull-TimeSenior
Salary110,000 - 140,000 USD per year
Apply NowOpens the employer's application page
Job Details
- Required Skills
- Docker
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.
View Full Description & ApplyYou'll be redirected to the employer's site
