Model API gateway
Build a server-side model endpoint with schema validation, secret isolation, timeout handling, usage telemetry, and a deterministic fallback.
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BEAT IT · CAREER LEARNING
AI Engineer
Build AI that solves real problems.
Video · Reading · AI Tutor · Resources · Labs · Real Job PracticeYOUR LEARNING STUDIOAI Engineer Job Simulation
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Career fieldwork
AI engineering labs, production tickets & capstone
Evaluation harness
Create a small golden dataset and score correctness, groundedness, refusal behavior, latency, and cost across prompt or model versions.
Permission-aware RAG pipeline
Ingest documents with metadata, chunk and index them, enforce authorization before retrieval, and return cited grounded answers.
Prompt-injection defense lab
Test direct and indirect injection cases, untrusted retrieved content, sensitive-data requests, and safe output handling.
Controlled tool-calling agent
Expose one least-privilege business tool with validated arguments, authorization, human approval for consequential actions, audit logs, and stopping rules.
AI observability and cost lab
Instrument request correlation, model latency, tokens, estimated cost, retrieval/tool failures, evaluation signals, and rate-limit events.
Provider outage exercise
Simulate timeout, rate-limit, malformed output, and provider outage conditions; verify retry boundaries, fallback, user messaging, and rollback.
Production readiness review
Run a threat, privacy, quality, reliability, accessibility, cost, and operations checklist before release.
Workplace ticket queue
Hallucinated policy answer
Reproduce the failure, identify retrieval or prompting root cause, add a regression evaluation, correct the behavior, and document evidence.
Unauthorized RAG result
Trace identity and metadata filters, prove the access-control gap, fix authorization before retrieval, and add cross-user negative tests.
Agent attempted an unsafe action
Review tool schema and permissions, add server-side authorization and approval boundaries, test adversarial arguments, and preserve an audit trail.
AI cost and latency spike
Use telemetry to isolate model, prompt, retrieval, or retry causes; implement a measurable optimization without weakening quality or safety.
Provider degradation incident
Confirm impact from logs and traces, activate fallback or graceful degradation, communicate status, and produce a short incident review.
Portfolio capstone
Secure enterprise RAG assistant with controlled actions
Design, build, evaluate, secure, operate, and document an internal knowledge assistant that retrieves only authorized content, cites its evidence, and can invoke one approved business tool through a human-controlled workflow.
Problem statement, users, success metrics and acceptance criteriaCurrent and target architecture diagrams with trust boundariesData-source inventory, ownership, retention and permission modelIngestion, chunking, metadata and vector-index designPermission-aware retrieval implementation with citationsServer-side model gateway with validated structured outputsEvaluation dataset and scored baseline/regression resultsPrompt-injection and sensitive-data security test evidenceLeast-privilege tool contract with authorization and human approvalLogs, traces, latency/token/cost dashboard or evidenceRate limits, failure handling, fallback and rollback planThreat model and responsible-AI reviewOperations runbook and incident-response checklistExecutive demo plus portfolio README explaining decisions and limitations
Day 1
Inventory models, providers, prompts, data sources, vector stores and tools
Request only required access and identify environment/secret boundaries
Locate evaluations, telemetry, budgets, runbooks and current incidents
Run the existing application and reproduce one known workflow before changing code
Week 1
Complete a scoped AI engineering ticket with tests and evaluation evidence
Trace one request across API, retrieval/tool layer and model provider
Add or improve a regression evaluation and security negative test
Participate in code review and document operational impact
Month 1
Own a small production AI feature from requirements through release
Demonstrate quality, security, cost and reliability with measurable evidence
Handle or simulate an AI production incident and improve the runbook
Present architecture, tradeoffs, limitations and next improvements to technical and business stakeholders
Before you call yourself job-ready
Can you explain where deterministic code should replace model reasoning?Can you prove retrieval respects the requesting user's authorization?Can you evaluate an AI change against a repeatable dataset instead of a demo?Can you prevent model-generated tool arguments from bypassing server authorization?Can you trace latency, tokens, cost and failures for one request?Can you explain the system's fallback and rollback behavior?Can you show security tests for prompt injection and sensitive-data exposure?Can another engineer operate the system from your documentation?
Course overview, access & enrollment
BEAT IT TRAINING & STAFFINGAI Engineer Job Simulation
Build a document assistant from discovery through deployment and demonstration.
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