AI Engineering

Interview prep for AI engineering and solutions-architect roles applying agentic AI in regulated finance — AML, KYC, sanctions, RegTech. 23 pages covering MCP, harnesses, RAG, evals, error handling, and audit trails, plus a 28-question drill set and four hands-on MCP build tutorials.

Senior-level interview prep for an AI Agents Solutions Architect role embedded in Global Finance — agentic automation of close, reconciliations, treasury, audit prep, and SOX-controlled workflows using Claude API, n8n, Python, and MCP, integrated with NetSuite, BlackLine, Kyriba, Fireblocks, and Lukka.

Senior-level interview prep for an AI Agents Solutions Architect role in HR / People — agentic workflows over Workday, ATS, payroll, and ticketing that respect employee PII, payroll calendars, and cross-jurisdictional privacy obligations.

Senior-level interview prep for a GPU and accelerator infrastructure role — clusters, schedulers, vLLM / Triton / TensorRT, GPU observability and cost, and the production substrate that lets the org train, evaluate, and serve models in-house.

Senior-level interview prep for a high-scale Rust AI Infrastructure SWE — model inference, orchestration, and execution layers powering agent systems at millions of users with strict reliability, latency, and correctness standards.

Hands-on build tutorial. From cargo new to a production-shaped streaming axum service that fronts vLLM with batching, circuit breakers, request hedging, and OpenTelemetry tracing. Walk away with a working repo.

A deep dive into how vLLM's PagedAttention actually works — KV-cache layout, why blocks, fragmentation math, prefix caching, and the difference between throughput and goodput. No interview framing, just the concept explored properly.