Become a Modern Engineer for the AI-native future

Build a .NET document extraction gate that treats OCR output as uncertain evidence, validates required fields, tables, confidence, and totals, and quarantines weak records before they reach a workflow.

Build a Python expense-policy workflow that uses Jev for typed semantic decisions while deterministic code owns amounts, dates, duplicates, thresholds, audit records, and final routing.

Build a .NET batch inference runner with request manifests, idempotency keys, retry envelopes, checkpoints, failed-row quarantine, and replayable output records backed by Microsoft Foundry.

Treat RAG embedding indexes as deployable artifacts with current and candidate manifests, recall regression checks, metadata validation, snapshots, restore drills, lineage, and promotion evidence.

Build a local Feast feature store with point-in-time training retrieval, SQLite online serving, freshness gates, and deterministic scoring for hybrid AI decisions.

Build a .NET streaming AI gateway with server-sent events, cancellation handling, partial-output policy, and finalization records before streamed text is treated as committed.

Build a local Python runtime-control layer that uses feature flags to select model routes, prompt versions, tool authority, retrieval corpora, reasoning modes, and kill switches before an AI workflow executes.

Build a .NET data-contract pipeline that admits only fresh, owned, audience-safe, lineage-backed insurance policy documents into RAG or agent context.

Turn chain-of-thought from a prompt habit into an AI engineering contract with private reasoning modes, visible rationales, evidence, logging policy, and evaluation checks.

A practical guide to building an AI engineering portfolio that demonstrates system judgment, operational tradeoffs, and production readiness without relying on flashy demos.