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AI Engineer Skills Checklist 2026: What to Screen For at Every Level
Every AI Engineer resume says the same things now. RAG. Agents. LLMs. Prompt engineering. The keywords have stopped meaning anything. This checklist is built for hiring managers, not candidates: nine skill areas, checked at Mid, Senior, and Staff / Founding level.
Ch. 4
The AI Engineering Hiring Guide
Every AI Engineer resume now says the same things. RAG. Agents. LLMs. Prompt engineering. The keywords have stopped meaning anything, and screening on them lets through people who've followed a tutorial while filtering out people who've quietly shipped the real thing.
This checklist is built for hiring managers, not candidates. Use it three ways:
To screen resumes — what evidence should be on the page at each level.
To sanity-check your job description — so you ask for the level you actually need.
To structure interviews — every item is something you can probe in conversation.
It's organized into nine skill areas, each with checks at three levels: Mid, Senior, and Staff / Founding. Junior candidates are covered briefly at the end, since most early-stage companies shouldn't make a junior their first AI hire.
A candidate should tick nearly everything at their level and most of the level below. Gaps at their own level are fine to probe; gaps below it are a warning.
How to read the evidence
For each skill, ask where the evidence comes from. A senior candidate should have production evidence for most of the list; a mid-level candidate can lean more on public work and prototypes.
Shipped, used by real users, maintained over time.
Open-source, write-ups, talks, side projects others can inspect.
Built and demoed, but never carried the weight of production.
Useful, but not evidence of the skill.
Software engineering fundamentals
The model is one component. Everything around it still has to be built properly.
- Writes clean, tested, production-quality Python (TypeScript is a strong plus)
- Builds and consumes APIs; comfortable with async and concurrency
- Works with relational databases and at least one cache or queue
- Uses version control, code review, and CI as a matter of course
- Designs services that handle failure: retries, timeouts, fallbacks, idempotency
- Has owned observability for a system: logging, tracing, alerting
- Makes sound build-vs-buy decisions on infrastructure
- Raises the engineering quality of the people around them
- Sets engineering standards for a team or company
- Designs systems that other engineers can extend safely
- Balances speed and technical debt deliberately, and can explain the trade-off to a board
Resume signal: named systems they built, not just teams they were on. "Built" beats "worked on."
Foundation model fluency
Knowing how models behave, fail, and differ.
- Has built with at least two model providers (e.g. OpenAI, Anthropic, Google)
- Understands tokens, context windows, temperature, and structured outputs
- Knows the difference between frontier, small, and open-weight models, and when each fits
- Uses tool calling / function calling confidently
- Chooses models based on measured task performance, not leaderboards or habit
- Understands long-context failure modes (e.g. information lost in the middle of a prompt)
- Has self-hosted or served an open-weight model, or can explain clearly when they would
- Keeps current: can name what changed in the model landscape in the last six months
- Designs a model-agnostic architecture so the company isn't locked to one provider
- Sets the company's model strategy: which capabilities to bet on, which to wait for
Interview probe: "What's something you believed about models a year ago that's no longer true?"
Retrieval and context engineering
Getting the right information in front of the model at the right moment.
- Has built a working RAG pipeline end to end: ingestion, chunking, embedding, retrieval
- Understands vector search and has used at least one vector store
- Knows that chunking strategy materially changes answer quality
- Has used hybrid search (keyword + semantic) and re-ranking in production
- Has debugged retrieval that returned the right documents but produced wrong answers
- Handles citations, grounding, and "the answer isn't in the documents"
- Knows when retrieval is the wrong solution entirely
- Designs context architecture across products: what the model sees, from where, and why
- Treats proprietary data and its retrieval as a competitive moat, and builds accordingly
Interview probe: "Your RAG system retrieves the right chunks but answers are still wrong. What do you check?"
Agents and orchestration
Systems where the model plans, uses tools, and takes multiple steps.
- Has built a multi-step workflow with tool calls
- Understands the basic agent loop: plan, act, observe, repeat
- Knows at least one orchestration framework, and when not to use one
- Has shipped an agent that real users relied on
- Manages state, memory, and context across steps
- Builds guardrails: step limits, cost limits, human approval for irreversible actions
- Can explain why agents fail more than single calls (compounding error, drift, loops)
- Knows when a simple deterministic workflow beats an agent
- Designs agent architecture across a product, including multi-agent systems where justified
- Defines how the company evaluates and trusts autonomous actions
Red flag: everything is an agent. Strong engineers reach for agents last, not first.
Evaluation
The single most important section on this list. An engineer who can't measure quality can't tell you whether your product is getting better or worse.
- Has built an evaluation dataset for an LLM feature
- Defines metrics that match the task, not just generic scores
- Runs evals before and after changes to prompts or models
- Has built an eval harness wired into CI, gating prompt and model changes
- Uses LLM-as-judge appropriately, and knows how judges can be biased or wrong
- Turns production failures and user feedback into new test cases
- Has caught a regression with evals that would otherwise have shipped
- Evaluates multi-step agent trajectories, not just final outputs
- Sets the company's quality bar and how it's measured
- Builds evaluation culture so every engineer treats evals as part of the work
Hard gate: if a Mid-level or above candidate has never built evaluation, stop the process. This isn't a gap to train; it's a different job.
Cost, latency, and reliability
AI features have unit economics. Someone has to own them.
- Understands how token usage drives cost
- Uses streaming to improve perceived latency
- Handles provider errors, rate limits, and timeouts gracefully
- Has reduced inference cost materially in production, and can say how
- Uses prompt caching, semantic caching, batching, and model routing
- Has built provider fallbacks so a model outage doesn't take the product down
- Can walk through a monthly inference bill and explain where the money goes
- Owns AI unit economics: cost per user, per task, and margin impact
- Makes model and architecture decisions with gross margin in mind
Interview probe: "How would you cut our inference bill by 40% without users noticing?"
Fine-tuning and model adaptation
Not always needed, and rarely needed first. But a senior engineer should know when and how.
- Understands what fine-tuning does and doesn't do (it shapes behavior; it's a poor way to add knowledge)
- Knows the alternatives: better prompting, retrieval, few-shot examples
- Has fine-tuned a model, or can clearly describe the process and its trade-offs
- Knows when a fine-tuned small model beats a prompted large one (cost, latency, consistency)
- Understands parameter-efficient methods such as LoRA at a practical level
- Decides when the company should invest in its own models, and when it shouldn't
- Plans the data strategy that makes future fine-tuning possible
Note: if fine-tuning and training are core to your product, you may need an ML Engineer instead. See Chapter 2.
Safety and security
The ways an AI feature can embarrass you in front of a customer.
- Understands prompt injection and why untrusted input is dangerous
- Validates and constrains model outputs before they're used
- Has defended a production system against prompt injection and data leakage
- Designs permissioning so agents can't access or do more than they should
- Handles PII and sensitive data correctly in prompts, logs, and traces
- Has a plan for hallucination: detection, grounding, and graceful failure
- Owns AI risk for the company, including customer and compliance questions
- Can speak credibly to enterprise security reviews
Red flag: treats safety as an edge case.
Product judgment
The rarest skill on the list, and the one that most separates good from great.
- Understands the user problem behind the feature they're building
- Looks at real outputs and real traces, not just metrics
- Knows where a model adds value and where deterministic code is better
- Scopes AI features to what can be made reliable now
- Talks to users, and changes the design based on what they learn
- Can explain their work's impact in business terms
- Shapes the product roadmap based on what AI can and can't do
- Decides what the company should build, not just how
- Makes the company credibly AI-native, not a company with an AI feature
Interview probe: "Where in our product would you refuse to use a model?"
The one-page summary
What should a candidate clearly have at each level?
Staff / Founding: sets standards, sets model strategy, designs context architecture, agent architecture across product, sets the quality bar, owns AI unit economics, decides model investment, owns AI risk, and shapes the roadmap.
What good AI Engineer hiring looks like at each level, at a glance.
Junior candidates
Most Seed to Series B companies shouldn't make a junior their first AI hire. There's no one to set architecture, no evaluation culture to learn from, and mistakes are expensive.
If you're hiring a junior into an existing AI team, look for:
Strong software fundamentals — the rest can be taught
Something built and shipped with a model, even as a side project
Curiosity about why an output was wrong, not just whether it was
Evidence of learning fast: this field changes monthly
What not to screen for
Some things show up on resumes and job descriptions that tell you very little.
Years of "AI experience"
Foundation models are barely four years old. Depth matters; tenure doesn't.
A long list of frameworks
Frameworks change constantly. Judgment about when to use one doesn't.
Certifications
Rarely correlated with shipping ability in this role.
Research publications
Valuable for research roles. For product AI engineering, a shipped system beats a paper.
"Prompt engineering" as a headline skill
Every AI Engineer prompts. On its own, it's a junior signal.
Big-name employer alone
Ask what they personally built there.
Common AI Engineer hiring mistakes, and what to screen for instead.
Using this checklist on your job description
Run your job description against this list before you post it.
Pick the level honestly. If you need someone to own AI architecture, that's Senior at minimum.
Ask for that level's checks, not all three levels. A description that asks for everything describes nobody.
Name evaluation explicitly. It filters the field faster than any other requirement.
Cut what you won't use. If they'll never fine-tune, don't make it a requirement.
Say what's hard. Strong candidates are screening you with the same rigour.
The bottom line
The keywords are the same on every AI Engineer resume. The evidence behind them isn't. Screen for production evidence at the right level, make evaluation a hard gate, and weight product judgment more heavily than any framework or model on the list.
This checklist is updated monthly as the tools and expectations shift. The core — engineering discipline, measurement, and judgment — doesn't change.
Get a shortlist already screened against this checklist.
We use this exact framework to vet every AI Engineer we put in front of you.
Wayne Lindor
PRINCIPAL | PRIZED
HireAIEngineers.com is the AI engineering specialist division of Prized, placing niche engineering talent with the companies building the AI era, across AI Engineering, Robotics & Physical AI, and Liquid Cooling & Data Center Infrastructure.
Wayne heads up our AI Engineering practice, recruiting across LLM applications, agents, retrieval and evaluation, machine learning and research, and the ML infrastructure underneath — up to and including VP Engineering, Head of AI, Chief AI Officer and CTO level.
About this guide
The AI Engineering Hiring Guide
The AI Engineering Hiring Guide is published by HireAIEngineers.com, the AI engineering practice of Prized. It's written for founders, CTOs and Heads of AI who are hiring AI and machine learning engineers, and it's updated as the market moves.
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