Starting an AI engineering role feels different from a classic backend job. The codebase may include prompts, evals, vector indexes, and half-documented model configs. It is easy to spend ninety days “ramping” and have little to show in your first review.
This checklist is written for AI software engineers joining product or platform teams. Looking for your next role? Start with open AI jobs or filter LLM engineer and ML engineer listings.
Week 0: Set the game board
Before you write code, lock expectations with your manager:
- What does “success in 90 days” look like in one sentence?
- Which systems are sacred (billing, auth, prod models) vs. safe to experiment in?
- Who owns evals, prompts, data pipelines, and on-call?
Write those answers down. Ambiguity is the default failure mode in AI orgs.
Month 1: Detective phase — understand reality
Run the product as a user
Complete the happy path and at least three failure paths. Capture screenshots, latency, and confusing UX. For AI products, deliberately try adversarial inputs and edge cases.
Map the AI system
Draw a one-page diagram: client → API → retrieval/tools → model → post-processing → storage. Note model names, prompt locations, and where logs/traces live.
Read the last three postmortems
If none exist, ask what almost went wrong. Production scars teach faster than READMEs.
Meet the humans
Schedule short chats with PM, design, data, and the person on-call for the AI path. Ask: “What do you wish the last hire had learned in month one?”
Month 2: Builder phase — ship a thin vertical slice
Pick one outcome that is visible and reversible:
- Improve an eval suite and gate a prompt change behind it
- Add tracing to a previously dark agent step
- Cut p95 latency or cost on a hot path with measurements before/after
- Fix a top user-reported failure mode with a regression test
Document the delta. AI work without numbers looks like vibes.
Establish a feedback loop
Create a weekly ritual: sample failures → label → update evals or prompts → ship. That loop is how strong AI teams compound.
Month 3: Amplifier phase — make the system better for everyone
- Write an internal playbook: “How we change prompts safely”
- Mentor or pair on one on-call shadow shift
- Propose a roadmap item tied to a metric (quality, cost, latency, or reliability)
- Share a short demo of what you shipped and what you measured
Anti-patterns that waste your first quarter
- Rebuilding the agent framework before shipping a user-visible fix
- Fine-tuning when prompting + retrieval + evals would do
- Collecting notebooks with no path to production
- Optimizing a model metric that nobody in the business tracks
Career tip: keep a brag doc
Every Friday, log: problem, action, metric, link. At review time you will thank yourself. The same doc becomes interview stories when you next browse remote AI roles or staff-level openings.
FAQ
I am joining as a research-leaning engineer. Does this still apply?
Yes—swap “user-visible slice” for “reproducible experiment + clear recommendation to product.” Impact still needs a decision, not just a paper draft.
How technical should my first project be?
Technical enough to earn trust, small enough to finish. A measured 10% latency win beats an unfinished platform rewrite.
Where should I look for AI engineer jobs that match this skill set?
Use Nova AI Jobs and filter by role category and experience. Pair job search with a public write-up of an eval or tracing project.
