Backend Engineer (AI era)
From language fundamentals to AI-augmented services: build systems that are correct, observable, and ready to call models.
Roadmap progress
0% 0 of 10 steps done
How to read the signals
- 1
A typed language + git
Go, TypeScript, or Python with types. Master the language before the framework.
Importance High Market demand High Automation risk Low - 2
HTTP & REST APIs
Status codes, idempotency, pagination, versioning. The contract is the product.
Importance High Market demand High Automation risk Medium - 3
Relational databases & SQL
Schema design, indexes, transactions, query plans. Postgres is a safe default.
Importance High Market demand High Automation risk Low - 4
Auth & security
Sessions vs tokens, OAuth2/OIDC, hashing, OWASP Top 10. Don't roll your own crypto.
Importance High Market demand High Automation risk Low - 5
Testing & CI
Unit, integration, and contract tests in a pipeline. AI writes tests fast — you decide what to verify.
Importance High Market demand Medium Automation risk High - 6
Caching & message queues
Redis, idempotent consumers, the outbox pattern. Decouple to scale.
Importance Medium Market demand High Automation risk Low - 7
Observability
Structured logs, metrics, traces (OpenTelemetry). You can't fix what you can't see.
Importance High Market demand High Automation risk Low - 8
Containers & deployment
Docker, a registry, and one orchestrator or PaaS. Reproducible beats 'works on my machine'.
Importance High Market demand High Automation risk Medium - 9
System design
Trade-offs, consistency models, capacity. The senior interview and the senior job.
Importance High Market demand High Automation risk Low - 10
LLM-backed services
Call models safely from a backend: streaming, retries, cost & token budgets, prompt injection defense.
Importance High Market demand High Automation risk Low