Senior AI Platform Engineer, Core Cloud Engineering

Who We Are

Vultr is on a mission to make high‑performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators around the world. With 32 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage solutions. In December 2024 Vultr announced an equity financing at a $3.5 billion valuation.

Founded by David Aninowsky and self‑funded for over a decade, Vultr has grown to become the world’s largest privately‑held cloud infrastructure company.

Vultr Cares

• 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

• Commitment matters to Vultr! 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

Join Vultr

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.

Key Responsibilities

• Evaluate and curate open‑source models — Llama, Mistral, Qwen, Deep Seek, 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 Git Lab 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.

Qualifications

• 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 Git Lab 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…