AI Engineering · Elite
AI Application Layer Engineer → AI Lead Engineer
A 50-hour cohort bootcamp that turns a Python-literate developer into a production-ready AI Application Layer Engineer on a clear path to AI Lead Architect. Build progressively from ML/DL foundations and prompt control through RAG, agent orchestration, multimodal and multibot systems, security, and full MLOps deployment. Every module pairs concept teaching with hands-on labs, culminating in a deployed, monitored, multi-agent RAG capstone with a security and evaluation layer.
Curriculum
What you’ll learn
1. AI Application Layer Foundations5 lessons
The AI application layer mindsetWhere value is created in LLM apps, the model vs. app layer split, and the top-1% engineer posture.Free previewFrom transformers to embeddings: just-enough theoryWorking intuition for transformers, attention, and embeddings without the full math derivation.
Reference architecture: client, orchestration, model, tools, evalThe canonical AI app-layer architecture and where each concern lives.
Lab: ship a hello-LLM app with cost loggingStand up a reproducible Python env, make your first completion, and log tokens and cost per call.
Assignment: architect and scaffold your chosen productDecompose a reference AI product into an architecture diagram and produce a working scaffold repo.
2. Advanced Prompt Engineering & LLM Control5 lessons
Structured prompting: roles, few-shot, XML/JSON outputsSystem vs. user vs. tool messages; few-shot patterns; forcing structured outputs.
Determinism controls: temperature, stop sequences, schema-validated outputsDecoding controls, JSON-mode, schema retries, and context-window budgeting.
Demo: schema-validated JSON outputs with retriesForce reliable structured outputs with Pydantic + instructor-style retries.
Lab: build a 10-prompt eval harnessBuild a notebook with 10 test prompts plus automatic eval metrics (accuracy, format-validity, latency).
Assignment: prompt eval study with ranked resultsRun a controlled study comparing prompts, rank by metric, and justify the chosen prompt.
3. RAG Systems (Retrieval-Augmented Generation)5 lessons
Why RAG: vs fine-tuning vs long contextDecide when RAG is the right tool and what its failure modes look like.
Chunking, embeddings, and vector databasesChunking strategies, embedding model choice, and trade-offs across vector stores.
Demo: simple RAG with citationsBuild a simple RAG pipeline over a document set with grounded citations.
Lab: advanced RAG with hybrid search, re-rank, query rewriteUpgrade to advanced retrieval and measure the quality lift with Ragas.
Assignment: Dockerized RAG microservice with eval reportShip a /query service plus a Ragas report comparing simple vs. advanced retrieval.
4. LLM Orchestration & Agent Frameworks5 lessons
Workflows vs. agents: when to choose whichPick the right abstraction for the job and avoid agent-shaped overengineering.
Tool use, function calling, reasoning loopsReAct, plan-execute, state, memory, and cost/loop control.
Demo: single tool-using agent with guardrailsA tool-using agent with retries, validation, and graceful failure.
Lab: two-agent supervisor/worker workflowBuild a supervisor/worker pair that completes a multi-step task with a stopping condition.
Assignment: LangGraph multi-agent task solverShip a LangGraph repo with a state diagram, tool registry, and a successful run trace.
5. AI Automation & Workflow Engineering5 lessons
Event-driven AI: triggers, steps, idempotencyWorkflow vs. agent boundaries, queues, retries, and idempotency.
Human-in-the-loop approval gates and audit logsWhere to put approvals, how to audit decisions, and the cost/rate-limit envelope.
Demo: document intake to extract, summarize, routeAutomate a real document-handling pipeline end to end.
Lab: automation service with retries and audit trailTrigger plus three or more steps plus retries plus an audit log.
Assignment: end-to-end automation pipelineShip an event-driven automation service with HITL approval and observability.
6. Multimodal AI Applications5 lessons
Vision, ASR, TTS, and multimodal RAG basicsHow to use vision/audio model capabilities and when each modality wins.
Demo: screenshot-to-structured-data extractorParse a real screenshot (kanban, dashboard) into clean JSON.
Lab 1: voice-note to transcription to action itemsPipeline a short audio file through ASR and summarize into action items.
Lab 2: multimodal RAG over screenshotsIndex images alongside text and answer questions with grounded references.
Assignment: multimodal endpoint with testsShip an API that accepts an image or audio file and returns structured output.
7. Multibot & Conversational Bot Systems5 lessons
Conversation state, memory, and turn-takingDesigning memory, context windows, and conversation lifecycle.
Multi-persona routing and handoffRouting intent across specialized personas with clean handoffs and fallback.
Demo: streaming voice or chat botStreaming token/audio output with barge-in and turn detection.
Lab: multi-persona router with two or more personasA router bot coordinating support, sales, and triage personas with handoff.
Assignment: deployed multibot demo with transcript logShip a deployed multibot with persistent memory and a transcript audit log.
8. AI App Security & Responsible AI5 lessons
OWASP LLM Top 10 and threat modeling for AIHow AI apps fail and how to threat-model before you ship.
Prompt injection, jailbreaks, and data exfiltrationDirect and indirect injection, tool-use abuse, and exfil paths.
Demo: guardrails for input/output validation plus PII redactionAdd input filtering, output validation, allow-listed tools, and PII redaction.
Lab: red-team your own RAG/agent appRun a structured red-team against your own app and document the findings.
Assignment: security report with before/after guardrail testsThreat model plus exploit log plus before/after guardrail test results.
9. Production Deployment & MLOps for AI Apps5 lessons
FastAPI service design: JWT auth, rate limits, structured logsThe production-quality API layer for an AI service.
CI/CD with eval-in-CI as a merge gateAutomated evals that block regressions before they ship.
Demo: Dockerize and deploy to a cloud runtimeContainerize the AI service and ship it to ECS/Fargate (or equivalent).
Lab: CI pipeline with Langfuse tracing and RagasWire tracing plus eval to the CI pipeline; block bad merges.
Assignment: production-ready deployed serviceDockerized service with CI/CD eval gate, JWT and rate limit, and a live tracing dashboard.
10. Capstone: Deployed Multi-Agent RAG Application6 lessons
Capstone scope and architecturePick a domain via the Signal Project flow; produce a problem brief, architecture diagram, and success metrics.
Build the RAG core: advanced retrieval and eval baselineShip a working advanced-RAG service over your chosen corpus with a measured baseline.
Integrate multi-agent orchestrationAdd a LangGraph orchestrator with tool use over the RAG core.
Add multimodal/bot interface plus security layerLayer in a multimodal or conversational interface and apply guardrails.
Deploy, monitor, and evaluateShip to a cloud runtime with CI/CD eval gate, JWT and rate limit, and live tracing.
Demo, defense, and portfolio writeupFive to eight minute demo plus deck plus repo plus written report.
Your instructor
Chris
Founder & CEO of Novus Aegis Institute. Practicing AI application engineer with deep experience shipping LLM systems in production; leads the institute's AI engineering track from foundations through to lead-architect outcomes.
Enter the institution shaping the AI era.
Hands-on courses and guided paths for engineers who want to build and defend real AI systems.