Ship production AI and land your first role. Over three weeks, containerize and deploy agents behind FastAPI, implement security guardrails and full observability, then build a portfolio-worthy capstone and prepare for AI engineering interviews.
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3 weeks
What You'll Learn
Expose AI agents as production FastAPI endpoints inside Docker containers
Implement prompt injection defense using NeMo Guardrails and PII masking with Lakera Guard
Trace token usage and latency across agent steps using Langfuse
Document production system architectures that stand out in technical interviews
Deploy a production-grade AI API on a cloud platform
Course Content
W1
Week 1: Deployment & APIs
Package your agent and ship it as a real, callable API.
1
Exposing Agents via FastAPI
Wrapping agent logic in FastAPI endpoints with request validation, async handlers, and OpenAPI documentation.
2
Efficient Dockerfiles for ML
Writing multi-stage Dockerfiles that minimize image size and build time for ML environments with large dependencies.
3
Lazy Model Loading
Loading models on first request rather than startup to minimize container cold-start latency.
4
Container Orchestration & Readiness Probes
Configuring health checks and readiness probes so orchestrators only route traffic to fully initialized containers.
Weekly Win
Cloud Deployment
Deploy a containerized AI agent to a cloud platform (e.g., AWS ECS) and verify it handles live requests end-to-end.
W2
Week 2: Security, Guardrails & Observability
Make your AI system safe, measurable, and auditable.
1
Prompt Injection Theory & Jailbreaks
Understanding how adversarial inputs exploit LLM instruction-following to bypass system prompts and safety rules.
2
Content Safety with NeMo Guardrails
Defining topical rails and content policies in NeMo Guardrails to block off-topic and harmful agent responses.
3
PII Masking & API Security via Lakera Guard
Detecting and redacting personally identifiable information and enforcing API-level security using Lakera Guard.
4
Tracing Latency & Tokens with Langfuse
Instrumenting agent pipelines with Langfuse to trace per-step latency, token consumption, and cost per request.
Weekly Win
Ex-Post Evaluation & Anomaly Alerts
Configure automated anomaly alerts in Langfuse that fire when output quality scores drop or latency spikes beyond a threshold.
W3
Week 3: Career Prep & Capstone Portfolio
Turn your projects into a portfolio that gets you hired.
1
Documenting End-to-End Architectures
Writing architecture documentation that communicates design decisions, trade-offs, and scaling strategies to hiring managers.
2
System Design for AI
Solving AI system design problems covering latency, throughput, statefulness, and failure modes in a whiteboard format.
3
AI-Centric System Design
Designing feature stores, model versioning systems, and A/B testing infrastructure for production ML platforms.
4
Behavioral Interviews & Stakeholder Communication
Framing technical decisions in business terms and answering behavioral questions with structured STAR narratives.
Weekly Win
Capstone: Production AI API
Deploy a fully documented, production-grade AI API with guardrails, observability, and a written architecture document ready to share with employers.