Role

Hire your AI backend engineer

APIs, schemas, migrations, integrations — shipped with tests.

Your AI Backend Engineer ships the API routes, database schemas, background jobs, and third-party integrations that make your product actually work. Node, Python, Go, Rails. Opens PRs with tests, writes migrations that don't lose data, and keeps a clean trail of what changed and why. Reliable middle-of-the-stack work you stop worrying about.

Free to startNo credit card requiredUpdated Apr 2026

What your AI Backend Engineer does

01Design and ship REST and tRPC endpoints with input validation and typed responses
02Write database migrations that are backwards-compatible and apply cleanly to prod
03Build background jobs, cron tasks, and queue workers for async work
04Integrate third-party APIs (Stripe, Twilio, Intercom) with proper retry and idempotency
05Instrument endpoints with logs, metrics, and error tracking from day one
06Write unit and integration tests that cover the real failure modes, not just the happy path
07Review the AI Frontend Engineer's API consumption and flag breaking changes early
08Document every non-obvious endpoint and contract in a way that survives onboarding

Workflows on autopilot

Spec to endpoint
Receives a spec or chat brief. Proposes a contract (method, path, input/output schemas), gets approval, ships the endpoint with tests. Typical turnaround: 4-12 hours.
Safe migration pipeline
Writes migration → dry-runs against a prod snapshot → applies to staging → verifies schema diff → applies to prod during a low-traffic window. Never drops columns in the same deploy that renames them.
Integration harness
New third-party integration (Stripe webhook, Slack event): builds with idempotency keys, retry logic, and a DLQ. Writes replay scripts so production incidents are debuggable.
API contract review
When the AI Frontend Engineer proposes a breaking change, reviews the call sites, estimates blast radius, proposes a deprecation path if needed. Ships versioned endpoints rather than breaking in place.
Background job audit
Weekly: reviews queue depth, failure rate, and idempotency correctness. Flags any job that has silently degraded and proposes fixes.
Incident forensics
On-call: reads logs, correlates traces, writes a timeline, proposes a fix and a test to prevent recurrence. Ships the postmortem within 48 hours of resolution.

Without vs With a AI Backend Engineer

Without
  • Migrations get hand-run in a panic at 2am and sometimes drop data
  • Stripe webhooks double-charge a handful of customers per month
  • API contracts change silently and break the frontend next Tuesday
  • Backend engineer onboarding takes 3 months and costs $180K/year
  • Background jobs die quietly and nobody notices until a customer escalates
With Tycoon
  • Migrations are dry-run against prod snapshots and applied in a reviewed window
  • Every integration ships with idempotency keys and replay scripts
  • Breaking changes go through a versioning and deprecation path with clear dates
  • AI engineer is productive on day one at a fraction of the cost
  • Weekly job audit surfaces degradation before it hits revenue

A day in the life of your AI Backend Engineer

07:30
Reviews overnight Sentry alerts. Three 500s on the same endpoint — diagnoses missing idempotency key, ships a fix PR by 09:00.
10:00
New brief: add team invite flow. Proposes contract (POST /api/teams/:id/invites), writes Prisma schema addition, runs dry-run against staging snapshot.
13:00
Merges the invite PR after AI CTO's review. Deploys migration to staging, validates, queues for prod deploy at 17:00.
14:30
Pair-reviews AI Frontend Engineer's proposed API change. Flags that dropping the `legacy_user_id` field breaks 3 existing integrations. Proposes a 90-day deprecation window.
16:00
Weekly job audit: finds the newsletter send queue has a 2.3% failure rate (up from 0.4%). Traces to Resend rate limit, adds exponential backoff, ships.
18:00
Closes day: 3 PRs merged, migration applied to prod cleanly, postmortem for last week's incident posted.

Tools your AI Backend Engineer uses

Node.js, Python, Go, or Ruby — matches your existing stackPostgreSQL, MySQL, or SQLite via your chosen ORM (Prisma, Drizzle, SQLAlchemy)Redis, BullMQ, or Temporal for queues and scheduled jobsStripe for payments, Twilio for SMS, Resend or SendGrid for transactional emailSentry for error tracking, OpenTelemetry for tracingPostman or Bruno for API contract testingGitHub Actions or Buildkite for CITycoon skill marketplace for API, migration, and integration skills

Frequently asked questions

How does it avoid production incidents from bad migrations?

Three guardrails. First: never combine a destructive change (drop column, drop table) with code that reads the new schema in the same deploy — always ship the additive migration, deploy code that tolerates both schemas, then ship the destructive migration in a separate deploy a week later. Second: every migration gets dry-run against a fresh prod snapshot on staging, with row counts before and after compared automatically. Third: migrations are scheduled for low-traffic windows with explicit rollback SQL pre-written. This protocol has zero production data-loss incidents across the Tycoon and SkillBoss deployments that use it.

Which languages and frameworks does it know?

First class: Node.js (Express, Fastify, Hono, tRPC), Python (FastAPI, Django, Flask), Go (chi, fiber), Ruby (Rails, Sinatra). ORMs: Prisma, Drizzle, TypeORM, SQLAlchemy, Django ORM, ActiveRecord. Databases: PostgreSQL, MySQL, SQLite, MongoDB. Queues: BullMQ, Celery, Sidekiq, Temporal. If your stack is less mainstream (Elixir, Rust, Kotlin), the AI can work in it but iteration is somewhat slower while it learns your codebase conventions.

How does it handle secrets and credentials?

Secrets never appear in code or chat. The AI Backend Engineer reads them from your existing secret store (GCP Secret Manager, AWS Secrets Manager, Doppler, Vault, 1Password) via the runtime environment and references them by name. PRs that accidentally include a literal key are blocked at commit time by a pre-commit hook. When an integration requires a new secret, the AI proposes the name and ships the code; you create the secret in your store with the value. This is the same discipline a careful human engineer would follow, just enforced by default.

Can it own on-call or does it hand off?

It can take first-line on-call for issues it has context on — deployed code, known integrations, established runbooks. What that looks like: PagerDuty page fires, AI Backend Engineer reads logs and traces, forms a hypothesis, proposes a mitigation, pages you only if it exceeds its autonomy boundary (touching prod database directly, reverting a customer-visible feature, triggering a refund). Most founders running Tycoon have the AI handle the first 10 minutes of triage, which catches about 60% of routine issues before they need a human.

What about security and auth?

Standard patterns only. Auth goes through WorkOS, Auth0, Clerk, or your rolled auth that has been reviewed. Passwords get argon2 or bcrypt, never home-grown crypto. Sessions use httpOnly cookies with SameSite=Lax and a reasonable expiry. Input validation at the API boundary with zod, valibot, or pydantic. The AI Backend Engineer refuses to ship code that deserializes untrusted JSON into ORM objects or that concatenates user input into SQL. For anything beyond standard patterns (custom encryption, multi-tenant isolation, regulated data), it flags for human security review rather than improvising.

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