Technical interviews have a reputation problem. Candidates complain that they test memorised algorithms rather than real skills, while hiring managers complain that people who interview brilliantly sometimes struggle with the messy reality of live systems. Both frustrations point to the same issue: many interview formats measure the wrong things.
If your goal is to hire software developers, DevOps and platform engineers or AI and machine learning engineers who can contribute to production systems quickly, your interview needs to reveal how candidates think when things are uncertain, broken or constrained. Specialist firms such as prod ready recruitment build their screening around real production experience for exactly this reason. Below are twelve techniques you can use in your own process, along with tips on getting the most out of each.
1. The Experience Deep-Dive
Ask the candidate to choose a system they built or maintained and walk you through it in detail. Then keep asking follow-up questions: why was it designed that way, what broke, what would they change, how was it deployed and monitored, and who else was involved?
Engineers with genuine production experience can usually go several layers deep, describing trade-offs, failures and lessons learned. Those who have only touched a system superficially tend to run out of detail quickly. This technique works for every seniority level and every specialism.
2. Realistic Code Review
Give candidates a pull request that resembles something your team might actually see: a feature with a subtle bug, missing tests, a performance concern or an unclear variable name. Ask them to review it as they would for a colleague.
Code review reveals judgement. Strong candidates spot issues that matter, prioritise them sensibly and phrase feedback constructively. It also mirrors a task they will perform many times a week in the real job.
3. Incident Simulation
Present a scenario: an alert fires, error rates are rising and customers are complaining. Share sample logs, dashboards or metrics and ask the candidate how they would investigate. What would they check first? When would they escalate? How would they communicate with stakeholders?
This technique is especially valuable for DevOps, platform and backend roles. It shows whether a candidate has a calm, methodical approach to troubleshooting or has only read about incidents rather than handled them.
4. Collaborative System Design
System design interviews are common, but they are most useful when run as a collaboration rather than a monologue. Describe a problem similar to one your team faces, then work through it together, introducing new constraints as you go: a sudden traffic spike, a regulatory requirement, a tight budget.
What to listen for
- Clarifying questions before jumping to solutions.
- Awareness of failure modes, not just the happy path.
- Pragmatic choices rather than the most fashionable technology.
- Consideration of monitoring, deployment and maintenance.
5. Pair Programming on a Small, Real Task
Instead of a whiteboard puzzle, pair with the candidate on a short task in a realistic codebase and a language they are comfortable with. Let them use their normal tools, including documentation and search. You will learn how they read unfamiliar code, how they test and how they communicate while working.
Keep the task small enough to make progress in the time available. The aim is to observe working style, not to see who finishes fastest.
6. The Trade-Off Question
Ask candidates to describe a time they had to choose between doing something the ideal way and doing it quickly. What did they decide, why, and what happened afterwards? Did they return to pay down the technical debt?
Production engineering is full of trade-offs. Candidates who can articulate them clearly, and who understand the business context behind technical decisions, tend to make better choices under pressure.
7. Deployment and Release Walkthrough
Ask the candidate to describe, step by step, how code they wrote got from their laptop into production at a previous job. Which checks ran? How were releases rolled out? How would a bad deployment be detected and rolled back?
Engineers who have only worked on side projects or in environments where someone else handled releases may find this difficult. For roles where engineers own their code in production, it is a revealing question.
8. Model-to-Production Discussion for AI Roles
For machine learning engineers, go beyond model accuracy. Ask how they have taken a model from experimentation to a live service. How was it packaged and versioned? How did they monitor performance after launch? What happened when input data changed over time?
These questions distinguish candidates with research or academic experience from those who have delivered models that real users depend on. Both backgrounds can be valuable, but the role requirements should decide which you need.
9. Testing Philosophy
Ask candidates how they decide what to test and how. What makes a good test? When have tests given them false confidence? How do they handle flaky tests in a pipeline?
A thoughtful answer suggests an engineer who cares about reliability rather than just passing a coverage target. It also tends to lead naturally into discussions about quality culture and code maintainability.
10. Learning From Failure
Invite candidates to describe a production mistake they made and what they learned. Most experienced engineers have caused an outage, deployed a bug or misconfigured something at some point. The question is how they responded.
Look for honesty, ownership and evidence of improvement, such as adding monitoring, writing a post-mortem or changing a process. Candidates who claim never to have made a mistake may simply lack production exposure.
11. Communication Under Pressure
Ask how they would explain a technical incident to a non-technical manager or customer-facing colleague. You can combine this with the incident simulation by asking them to draft a short status update.
Clear communication during incidents reduces confusion and builds trust across a business. It is a skill that separates good engineers from great teammates, yet it is rarely assessed directly.
12. Reverse Interview Time
Leave genuine time for candidates to question you. The questions they ask reveal what they care about. Engineers with production experience often ask about on-call arrangements, deployment frequency, observability tooling, technical debt and how incidents are reviewed.
This is also your chance to sell the role honestly. Strong candidates are assessing you just as carefully as you are assessing them.
Putting the Techniques Together
You do not need to use all twelve techniques in every process. A well-designed interview loop for most roles might include three or four, chosen to match the definition of production readiness for the specific job. For example:
- Backend software developer: experience deep-dive, code review, collaborative system design and deployment walkthrough.
- DevOps or platform engineer: incident simulation, deployment walkthrough, trade-off question and communication under pressure.
- Machine learning engineer: model-to-production discussion, experience deep-dive, pair programming and testing philosophy.
Score each stage against agreed criteria, collect interviewer feedback independently and make decisions quickly. A clear, efficient process is itself a signal to candidates about how your team operates.
Red Flags and Green Flags
Across all techniques, certain patterns tend to recur.
Green flags
- Specific, detailed stories about systems they owned.
- Comfort saying “I don’t know, but here’s how I’d find out”.
- Awareness of monitoring, rollbacks and failure modes.
- Collaborative, respectful communication.
Red flags
- Vague answers that rely heavily on buzzwords.
- Blaming others for every past problem.
- No awareness of how their code was deployed or supported.
- Dismissing testing, documentation or code review as unimportant.
Making Practical Interviews Fair and Inclusive
Realistic exercises are more predictive than puzzles, but they still need care to be fair. Candidates come from different backgrounds, use different tools and may be nervous in ways that do not reflect their day-to-day ability.
- Share the format in advance: tell candidates what each stage involves so they can prepare and perform at their best.
- Let people use familiar tools: allow their preferred language, editor and access to documentation wherever the exercise permits.
- Offer reasonable adjustments: ask every candidate whether they need anything to take part comfortably, and act on the answer.
- Use the same scenarios for comparable candidates: consistency makes scoring fairer and easier to calibrate.
- Train interviewers: make sure everyone understands the scorecard and knows how to probe without leading or intimidating.
Respecting candidates’ time
Experienced engineers are usually employed and busy. Long, multi-stage processes with unpaid take-home projects can deter precisely the people you most want to hire. Where possible, combine stages, schedule interviews close together and keep any assignment short and clearly scoped. If a longer exercise is genuinely necessary, consider paying for it. A process that respects candidates’ time sends a strong signal about how the team treats its engineers once they join, and it tends to reduce drop-out at the later stages where losing a candidate is most costly.
When to Bring in Specialist Help
Running interviews like these takes time from senior engineers, who are often your busiest people. Some teams choose to share the load with a recruiter that has technical depth in the relevant field. prodreadyrecruitment.com is one example of a UK firm focused on software development, DevOps and platform engineering, and AI and machine learning roles, with an emphasis on recruiters who have hands-on experience of the work they recruit for. Pre-screening of this kind can mean your engineers spend interview time only with candidates who have already shown credible production experience.
Conclusion
The best technical interviews look a lot like the job. Deep-dives into past systems, realistic code reviews, incident simulations and honest conversations about trade-offs and failures reveal far more about production readiness than abstract puzzles ever can.
Choose the techniques that fit your role, assess consistently and treat candidates with respect throughout. The result should be a process that is fairer for candidates, more predictive for your team and far more likely to produce hires who can contribute confidently to live systems from their first weeks.
