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Interview Prep — Agentic AI Engineering

This is the candidate's primary prep resource for the 14 roles in jd.md. It is built to do one thing: get you to walk into a Staff/Principal/Lead/VP agentic-AI interview and answer every question with a mechanism and a number, mapped to something you actually built in this track. Every claim here ties to a phase, a lab, and a figure from the Cheat Sheet.

The interviewer's real question is never "do you know what ReAct is." It is "if I put you on call for this platform, will you make the right tradeoff at 2 a.m. when an agent is looping, a tool secret leaked, a tenant's data surfaced in another tenant's answer, or the token bill tripled overnight?" These docs train the reflex to answer structurally, not from vibes.

The five documents

Deeper, full-length platform walkthroughs live in ../system-design/; this section is the drill and mapping layer that points into them.

How to use this (a 2-week and a 2-day plan)

If you have two weeks. Do one company per day from Battle Plans, in the application order below. Each morning read that company's plan and its heaviest phase's WARMUP.md; each evening run the matching coding drill and 15 rapid-fire cards. Do one full system-design case out loud, on a whiteboard, every other day. Prepare your five STAR stories once and reuse them.

If you have two days. Day 1: read the top-3 Battle Plans (Citi, Docker, Cohere/Temporal), the whole Cheat Sheet, and all of Rapid-Fire. Day 2: rehearse two system-design cases out loud and lock your STAR stories. Do not try to re-do labs in two days — instead, be able to describe each lab's core mechanism in three sentences (that is what the interview tests).

The one rule. Never give an answer without a number or a mechanism. "It depends" is a non-answer; "it depends on per-step reliability — at 0.95 you get ~13 unverified steps before you drop below a 50% end-to-end success target, so I'd checkpoint every 8 and add a critic" is a Staff answer. Turn every "it depends" into the variable it depends on. See the interview reflexes in the Cheat Sheet.

Study plan — company → heaviest phases

Each role in jd.md leans on a subset of the 18 phases. Study the primary phases until you can rebuild their lab from memory; skim the secondary ones for vocabulary. The "lead with" column is your opening positioning line.

Cross-cutting for all 14. Phase 00 (the 0.95^n reliability math and the trust boundary) is the substrate under every answer, and Phase 11 (evals) is asked in every platform role — "an agent you can't evaluate you can't ship." If you only have time for two phases, do 00 and 11.

The best application order

Straight from jd.md, highest-fit and highest-priority first. Apply in this order so your best-matched interviews happen while your prep is freshest:

1. Citi — Agentic AI Technical Lead (hybrid, Mississauga) — the single closest match to this track's language.
2. Citi — Lead Agentic AI Engineer, VP (hybrid, Mississauga).
3. Docker — Staff SWE, Agentic Platform (remote) — secure execution / MCP / evals.
4. Cohere — Senior SWE, Agent Infrastructure (remote) — platform reliability.
5. Temporal — Staff SWE, AI Foundations (remote) — durable execution / SDKs.
6. Redcan.ai — Staff SWE, Agentic AI Products (remote, Canada) — full-stack products.
7. Juniper Square — Staff SWE (AI) (remote) — SDK + guardrails + evals.
8. Wolters Kluwer — Senior Full-Stack, AI Platform & Agents — regulated GenAI.
9. OpenAI — Security Engineer, Agent Security (SF) — stretch; strong security match.
10. OpenAI — SWE, Agent Infrastructure (SF) — stretch benchmark.
11. Anthropic — Agent Prompts & Evals / Claude Code — stretch / research-lab benchmark.
12. LiveKit — Staff Rust SDK Engineer (remote) — if Rust/real-time interests you.
13. RBC — Staff/Lead Agentic AI — track for new postings (last one filled).

Rationale for the ordering, and the reasoning behind each pairing, is expanded per-role in 01-per-company-battle-plans.md.

Resume positioning (reuse across all 14)

From jd.md — the headline and bullets to anchor every application and every "tell me about yourself":

Principal Software Engineer focused on AI-native software delivery, agentic AI systems, LLM-enabled engineering workflows, context engineering, prompt standards, Spec-Driven Development, and secure human-in-the-loop validation.

Architected agentic LLM workflows with structured planning, tool invocation, context retrieval, prompt templates, validation loops, and reusable commands to support requirement discovery, implementation planning, code review, test generation, and delivery governance.

The Behavioral & Staff-Signal doc turns those bullets into STAR stories; the Battle Plans map them company-by-company.

Keyword coverage map (so nothing surprises you)

Every high-frequency keyword across the 14 JDs, and where in this track you can demonstrate (not just name) it:

Next: open 01-per-company-battle-plans.md.