AI engineers are expected to innovate at breakneck speed — but are held back by unreliable environments, glue code, and fragile ops scaffolding.
From managing GPU workloads to orchestrating microservices, the day-to-day feels more like cobbling together infrastructure than building intelligent systems.
It's time to say it clearly: AI engineers deserve better infrastructure — not "DevOps help when you ask for it," but smart, automated, developer-native platforms that understand the way AI workloads actually work.
Shipping AI ≠ Shipping Web Apps
Traditional infrastructure tooling assumes predictable traffic patterns, stateless services, and stable APIs. AI workloads? The opposite.
- Training jobs spike compute needs. Did you mean to create an alert every time?
- Model versions evolve quickly. (One hopes)
- Evaluation, rollout, and rollback aren't optional, they're core to safety and usability.
- Observability needs differ: latency is fine, but drift is fatal.
AI infra isn't just web infra with GPUs attached. And yet most tools were never designed for the AI loop.
The False Choice: Platform Fatigue vs DIY Glue Code
Most teams fall into one of two traps:
- 'One-size-fits-none' platforms that slow everyone down.
- Spaghetti-code setups built by overworked ML engineers on nights and weekends. 0-point chores that eat up an entire sprint.
What's needed: Composable, opinionated defaults that don't get in your way and infra that evolves with your workflows.
Observability from Day One
In AI, how a model fails is just as important as if it fails.
- Built-in support for evals, tracing, and metrics.
- Custom metadata tracking for every model version and agent run.
- Debuggable sandboxes with auditability — not just logs dumped into S3.
If you're flying blind post-deploy, you're not production-ready. MTTR is sometimes more important than MTBF
Permissioning That Doesn't Suck
AI teams aren't monoliths. You've got researchers, engineers, annotators, product owners and sometimes agents making API calls on their own. But most infra treats everyone (and everything) like the same user (ai-user LOL). That's at least one problem, pontentially more brewing:
Broad access permissions that create unnecessary security risks. You cannot beg an agent to please do read-only operations.
Development environments and repositories (!!) with production credentials
Missing visibility into system access and changes
Coarse-grained permissions that affect multiple systems
- 1Identity FirstUse SSO (e.g. Okta, Auth0, Google Workspace) across all infra and please no shared credentials. For services and agents, generate distinct scoped service accounts (e.g. 'read-only eval-bot').
- 2RBAC + ABACDefine roles for key personas (ML Researcher, Platform Engineer, Product Owner) and use labels/tags on resources to restrict actions contextually.
- 3Secrets & Credentials ManagementVault or AWS Secrets Manager to store API keys and tokens. Never pass secrets via environment variables in plain YAML.
- 4Per-Model Access PoliciesEach model version should have a registry record, audit logs, and ACLs for inference vs retraining.
- 5Agent & API HardeningUse short-lived tokens, set execution limits, trace each request path, and define a policy sandbox.
- 6Everything Should Be AuditableKeep logs of deployments, model accesses, prompt changes, and inference requests (with redacted data).
Zero trust is operational hygiene. With agents and models in the loop, AI teams need it even more.Learn more about implementing zero trust for AI workloads.
Rollouts Should Be Boring
Shipping a model should look like:
- Canary deploys to known-safe traffic
- Hot-reload support
- Blue/green deployments with rollback buttons
Right now? It's often a Slack thread and a manual Docker push. Engineers deserve better. Ops should feel boring, predictable, and safe. Iteration doesn't need to come with additional anxiety.
Meet You Where You Build
Infra should integrate with the tools you're already using (avoid tool sprawl whenever possible):
- Git-based workflows with CI/CD
- Model versioning and eval plugins
- Deployment via Terraform/Helm — not another custom DSL
- For teams using agents: provide secure, observable APIs or MCPs with scoped credentials. Zero-trust applies to agents too.
Bottom Line
AI is hard enough. Infrastructure shouldn't be, we've all been doing that for years.
If you're an AI engineer duct-taping your stack together, consider this a wake-up call — and a call to arms. You deserve infra that's flexible, safe, and actually understands your workflows.