HIGAET AI API Engineering
Learn to design robust APIs for AI services covering streaming, authentication, rate limits, versioning, and developer experience through practical backend labs.
Duration
6 weeks · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
AI API engineering is designing the robust interfaces other developers build on — versioned endpoints for language-model and embedding services with streaming, auth, limits, and great developer experience. It matters now because every assistant, search box, and automation depends on APIs that stay fast, predictable, and fair under load.
It is used to serve chat, embeddings, and streaming completions to apps used by a support team or a retailer. It solves versioning, token-event streaming with timeouts and reconnects, key management, and rate limiting with quotas and usage metering. It does not fix a weak model or bad retrieval behind the endpoint — an elegant API does not make answers more accurate — and it does not replace evaluation of what the API serves.
By the end you will be able to build a versioned REST API exposing language-model and embedding services, streaming endpoints with token events, timeouts, and reconnect handling, and a rate-limited, metered API with authentication and key-management flows.
Why this course exists
The gap is between an endpoint that works for one caller and a production API that versions cleanly, streams reliably, authenticates consumers, and enforces quotas under AI workload spikes. This course covers that part of the Model → Prompt → Context → Retrieval → Tools → Agents → Evaluation → Security → Infrastructure → Production arc — from model-service design through streaming and security to versioned, metered production infrastructure.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST API development
- Basic auth and versioning concepts
- Familiarity with streaming or backend deployment
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Module 01 — Foundations: API design principles for AI services
- Module 02
Module 02 — Core: request schemas, validation, and structured responses
- Module 03
Module 03 — Streaming: server-sent events, websockets, and partial results
- Module 04
Module 04 — Access: authentication, keys, scopes, and tenant isolation
- Module 05
Module 05 — Controls: rate limits, quotas, metering, and versioning
- Module 06
Module 06 — Quality: testing, load testing, and error handling
- Module 07
Module 07 — Operations: logging, monitoring, and developer documentation
- Module 08
Module 08 — Capstone: production-ready AI service API with docs and usage controls
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build versioned REST APIs that expose language-model and embedding services
- Design streaming endpoints with token events, timeouts, and reconnect handling
- Develop authentication and key-management flows for API consumers
- Deploy rate limiting, quotas, and usage metering for AI workloads
- Integrate validation, structured outputs, and error contracts consistently
- Evaluate API reliability with load tests and failure-injection exercises
- Secure AI endpoints against abuse, injection, and data leakage
- Optimize throughput and cost with batching, caching, and request routing
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Versioned language-model API
- Project 02
Token-streaming endpoint with reconnect
- Project 03
Key management and auth flow
- Capstone
Metered production AI API with quotas
Speak the language first.
- REST APIs for AI
- Web endpoints that expose language-model and embedding services with predictable request and response formats.
- API versioning
- Numbering schemes that let developers change endpoints without breaking existing consumers.
- Streaming endpoints
- Connections that send model tokens as events while they are generated, for faster-feeling responses.
- Token events and reconnects
- Stream messages carrying partial output, plus logic to resume cleanly after timeouts or disconnects.
- API authentication
- Key or token checks that verify which consumer is calling an AI service.
- Key management
- Issuing, rotating, and revoking API keys so access stays controlled.
- Rate limiting and quotas
- Caps on how many requests a consumer may send, protecting the service from overload.
- Usage metering
- Tracking tokens or calls per consumer so costs and billing stay accurate.
- Developer experience
- Docs, errors, and examples that make an API easy to adopt and debug.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Streaming responses cut off or hang
Check timeout settings and event framing, add heartbeat messages, and implement client reconnect with resume offsets.
Clients break after an API change
Compare versions and changelogs, restore backward compatibility or bump the version, and update migration docs.
Keys leak or stop working
Rotate the affected keys, audit where they are stored, and add scoped keys with expiry.
Rate limiter blocks legitimate traffic
Review quota tiers and burst settings against usage logs, then tune limits or add priority queues.
Usage metering disagrees with bills
Reconcile token counting logic with logged requests, fix double-counted retries, and backfill corrected totals.
Before you move on, you should be able to
- Build versioned REST APIs that expose language-model and embedding services
- Design streaming endpoints with token events, timeouts, and reconnect handling
- Develop authentication and key-management flows for API consumers
- Deploy rate limiting, quotas, and usage metering for AI workloads
- Explain how versioning and docs improve developer experience
- Evaluate API reliability under load and error conditions
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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View CourseReady to start HIGAET AI API Engineering?
A 6 weeks course — AI & Generative Intelligence.