About The Position
Vultr is seeking a highly skilled and experienced AI Platform Engineer to own the strategy and execution for embedding AI into the day-to-day workflows of our software engineering organization. The ideal candidate combines strong software engineering fundamentals with hands-on experience deploying LLM inference infrastructure and a genuine passion for accelerating how engineers work. This is your opportunity to leave your mark on the future of Cloud Infrastructure and transform how Vultr builds.
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
- 100% company-paid insurance premiums for employee medical, dental and vision plans.
- 401(k) plan that matches 100% up to 4%, with immediate vesting
- Professional Development Reimbursement of $2,500 each year
- 11 Holidays + Paid Time Off Accrual + Rollover Plan
- Increased PTO at 3 year and 10 year anniversary
- 1 month paid sabbatical every 5 years
- Anniversary Bonus each year
- $500 stipend for remote office setup in first year + $400 each following year
- Internet reimbursement up to $75 per month
- Gym membership reimbursement up to $50 per month
- Company paid Wellable subscription
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
Number of Employees
101-250 employees
