Sample Scorecard
One scorecard per candidate. Every shortlist.
A real example of the AI hard-skills report we deliver as part of every shortlist. Structured evaluation, per-question scores, English assessment, and a clear recommendation — reviewed by a human recruiter before delivery.
Interview Details
Oleksandr K. — Senior Backend Engineer
ID: 6f842308-4290-4433-ade2-9be***
Candidate Information
Name: Oleksandr K.
Email: o.k***@gmail.com
Status: completed
Created At: 3/3/2026, 9:53 PM
Position Details
Role: Senior Backend Engineer
Field: Backend & Infrastructure
Type: With Manager
Final Recommendation
Candidate demonstrates strong technical depth across distributed systems, database design, and API architecture with an average score of 8.7/10. Shows excellent understanding of system design tradeoffs, caching strategies, and microservice patterns. Minor gaps include limited experience with event-driven architectures and could provide more concrete examples of debugging production incidents.
English Assessment
Communication & clarity signal
A1
A2
B1
B2
C1
C2
Key takeaway
Communicates technical concepts clearly and fluently. Can lead architecture discussions and explain complex tradeoffs to both technical and non-technical stakeholders. Minor grammatical imprecisions that do not impede understanding.
Question-by-Question Evaluation
Overall Performance Summary
The candidate demonstrated strong foundational knowledge across all backend engineering topics with consistent performance scores of 8-9. Technical understanding was evident throughout, with particularly strong answers on database design and API architecture.
General Feedback
The candidate shows excellent practical knowledge of distributed systems, database optimization, caching strategies, and microservice design patterns. They consistently identified key tradeoffs and appropriate solutions across all areas. Their responses demonstrated real-world experience with production systems at scale.
Strengths
Demonstrates comprehensive practical knowledge of system design, database optimization, and API architecture with strong awareness of production-readiness concerns like monitoring, circuit breakers, and graceful degradation.
Areas of Improvement
Should provide more specific examples from past experience when discussing event-driven architectures, and could improve depth on theoretical distributed systems concepts like CAP theorem nuances.
Overall Assessment
The candidate shows senior-level working knowledge suitable for a backend engineering role with demonstrated ability to reason about system tradeoffs at scale.
Individual Question Evaluations
Candidate Answer:
I'd start by identifying the core requirements: short URL generation, redirection, and analytics. For the URL generation, I'd use a base62 encoding scheme with a counter service or pre-generated ID pool to avoid collisions. The read-heavy workload suggests a caching layer — Redis for hot URLs with an LRU eviction policy. For storage, a simple key-value store like DynamoDB or Cassandra for horizontal scaling. I'd put a CDN in front for popular links and use consistent hashing for the cache layer.
Evaluation:
Excellent system design answer covering all critical components. The candidate correctly identified the read-heavy nature, proposed appropriate caching strategy, and mentioned horizontal scaling considerations. The base62 encoding approach and pre-generated ID pool show practical experience with ID generation at scale.
Key Takeaway:
Strong system design skills with clear reasoning about scalability, caching, and storage tradeoffs. Production-ready thinking.
This is the AI hard-skills report we deliver per candidate, alongside the recruiter soft-skills notes and CV analysis. Generated as part of our 6-step shortlist process — every shortlist is signed off by a human recruiter.
For senior roles where cheating costs more
than missing a good hire.
Two layers of defense: AI Skills Validation tests how candidates actually use AI tools. Recruo Secure Browser ensures the test results you see are real.
78% of engineering teams now use AI daily
GitHub Copilot, Cursor, ChatGPT — AI-assisted development is the default. But most hiring pipelines still test for pre-AI skills only.
AI proficiency ≠ copy-pasting prompts
The best engineers know when to use AI, when to override it, and how to validate its output. That judgment is what separates a 2x engineer from a 10x one in 2026.
Bad AI habits cost more than no AI at all
Blindly trusting AI-generated code leads to security vulnerabilities, hallucinated logic, and tech debt that compounds silently for months.
AI Skills Validation
A dedicated module added to the technical interview: real coding tasks with AI tools available, followed by probing questions about the candidate's AI-assisted workflow. The result is a separate AI Proficiency Score alongside the standard technical evaluation.
How it works
The candidate gets access to Copilot/Cursor during part of the interview. We observe how they prompt, validate, and iterate — then score their AI fluency on a structured rubric.
AI-assisted coding
Can they use Copilot/Cursor effectively while catching errors?
Prompt engineering
Do they write precise prompts, or do they brute-force trial and error?
AI output validation
Can they spot when an LLM hallucinates, introduces a vulnerability, or produces subtly wrong logic?
AI-native architecture
Do they know when to reach for an LLM vs. a deterministic solution?
Recruo Secure Browser
Our proprietary anti-cheat interview environment. The candidate joins the AI technical interview through our locked browser session — preventing tab switching, screen sharing to a second device, and silent ChatGPT usage.
Why it matters in 2026
73% of candidates admit to using AI assistants during remote technical interviews. Without proctoring, a great-looking technical score may just be a great-looking ChatGPT prompt.
Locked browser session
Candidate cannot open new tabs, navigate away, or use external apps during the interview.
Copy-paste detection
Every paste from outside the session is flagged with timestamp and content fingerprint.
Eye-tracking & focus checks
Webcam-based attention monitoring detects looking off-screen for sustained periods.
Screen-share & process monitoring
Detects screen sharing to a second device, suspicious processes, and AI assistants running in the background.
Both included by default in Retained mandates. Available as add-ons for Standard and Fixed-fee plans.
Most used when hiring LLM engineers, RAG engineers, Evals engineers, ML platform engineers, Agentic AI developers or AI/ML engineers.