Senior AI Infrastructure Engineer

The posting, as Hellyeah AI wrote it

About Hellyeah AI
Hellyeah is the agentic operating system for distribution. AI agents plan, execute and optimize growth work, and humans approve it. A small engineering team runs several live products, with coding agents as part of how we ship.

The role
You will own the platform our products and agents run on: the gateway every model call passes through, the runtime for our production agents, and the clusters, data stores, pipelines, secrets and release paths underneath.

We're hiring two engineers, one in San Francisco and one in Shanghai. One will lean toward the AI platform and the other toward the core platform. You'll share ownership and on-call, and work closely with our CTO.

This role is onsite five days a week. On-call includes nights and weekends, and days often run past 10 hours.

Why this role: You'll own the whole platform, work directly with our CTO, and build the agents that run it.

We run production AI across text, speech and video, mostly through third-party model APIs, with some self-hosted serving. The gateway supports internal teams, our products and customers. We do not train models. If you only want to work on model training, look elsewhere. If you want to make a multi-provider AI stack reliable, secure and affordable, keep reading.

Product teams own product features. IT support, marketing sites and SEO sit outside this role.

What you'll do

The AI platform

• Run the model gateway. Own routing, fallbacks, provider failover, rate limits and rollouts for 90+ model deployments across OpenAI, Anthropic, Azure, Google and AWS Bedrock. Benchmark new providers on latency, quality, cost and failure behavior before they take traffic, and escalate to vendors when they fall short.
• Make the gateway multi-tenant. Give each team and customer the right keys, routes, limits and usage metering, and reconcile usage against provider invoices.
• Run the AI developer platform. Own shared coding-agent access, including seats, quotas and per-user limits, plus the remote workspaces our engineers use. One heavy user should never slow down everyone else.
• Run production agents safely. Own sandboxing, credential scoping and pinned releases for long-running agents on Kubernetes.
• Automate your own job. Expect to build a lot of autonomous infra workflows and infra agents that take on routine requests and alerts, with a person approving risky actions.
• Own infrastructure and AI spend. Reconcile provider bills against usage, tie spend to a team and product, and catch anomalies early. Right-size clusters, spend vendor credits before they expire, and handle quota and billing escalations with cloud and model vendors.

The core platform

• Keep clusters and cloud healthy. Debug and upgrade EKS, AKS and bare-metal k3s through Argo CD and Kustomize. Manage AWS, Azure and GCP through reviewed Terraform.
• Run the edge. Own Cloudflare across a dozen domains: DNS, WAF, TLS, CDN and object storage, plus GeoDNS, load balancers, egress I

[…]
ork to agents or automation, with guardrails, approvals and recovery when a run dies

• Enough Go or Python to read an unfamiliar codebase and ship a fix
• Hands-on observability: you can maintain Grafana, Prometheus and OpenTelemetry collectors, write metric and log queries, and build a dashboard people use

You'll also need

• GitOps and Terraform in production, ideally across more than one cloud
• Networking fundamentals: DNS, TLS, load balancing, WAF and routing
• You've taken a third-party service to production end to end, including SSO, backups and upgrades
• You've led a region, cloud or database migration with a tested rollback
• You've cut a real cloud or AI bill and can show the numbers
• Security judgment: least privilege, secret handling, credential rotation, safe rollouts
• Daily use of coding agents, plus clear views on what they should not touch unreviewed
• You follow the current agent landscape, including Hermes Agent, OpenClaw, open-source Codex and Vercel's eve, and can explain agent harness best practices: durable runs, sandboxing, scoped credentials, human approval, memory, tracing and evals
• The confidence to say no to a risky change and explain why, including to a founder
• Written English for daily work with teammates in the US, China and India

Nice to have

• LiteLLM or a similar LLM gateway
• LLM serving, such as SGLang or vLLM
• Multi-tenant API platforms, including metering and billing
• Data pipelines, such as Airbyte, Airflow or CDC into BigQuery
• Realtime audio or WebSocket services
• Agent sandboxing, such as gVisor, Landlock or agent-sandbox
• Langfuse or other LLM tracing
• Remote dev environments, such as Coder
• FinOps across several clouds
• Mandarin

How we hire
A detailed application form, an online assessment, a technical interview, a 30-minute deep dive with our CTO on a project you've built, then a paid work trial before any offer. You'll see how we work and we'll see how you work.