Build real AI applications, not just working code

AI Engineering Assessment

Building an AI application takes more than connecting an LLM and getting the right response. Candidates need to design how the system works, integrate the right building blocks, handle different scenarios and build something that performs reliably beyond the obvious test cases.

In this assessment candidates are given a problem statement and a fully configured virtual machine with the development stack required to build a functional AI application integrating an LLM and retrieval-augmented generation (RAG). Candidates can design the system, write code, test the application live and debug their solution.

Each assessment can be configured with or without an AI coding assistant. When enabled, it also helps evaluate how effectively candidates use AI coding assistant while building their solution.

Evaluate the complete solution across AI implementation, system design, code quality and test case performance, along with effective use of AI coding assistant when enabled. Assessment complexity can be adapted for AI Engineering roles across experience levels.

See how candidates think when AI is part of the process

AI-assisted Case Study Assessment

AI can surface information and suggest answers quickly. The harder question is whether candidates can identify the right problem, interrogate the evidence, challenge assumptions and turn AI-assisted analysis into a sound recommendation.

In this assessment candidates are given a detailed, job-relevant case study built around different kinds of problems and a wide range of information, including business data, customer feedback, market intelligence, competitor information and other role-relevant inputs. Candidates use an AI assistant to understand the case, explore the information, answer key questions, refine their thinking and build a recommendation, plan or decision with reasoning.

Evaluate both the quality of the candidate’s analysis and how effectively they work with AI. Assess the final response across Problem Identification & Definition, Analytical Framework & Structure, Data Interpretation & Insights, Strategic Recommendations, and Communication & Presentation.

AI Assistance Effectiveness is evaluated separately across Prompt Quality, AI Utilization, Research Effectiveness and Strategic Questioning.

Know who has depth, not just recall

AI-Evaluated Subjective Assessment

Some capabilities cannot be measured by selecting the right answer. Understanding, reasoning and judgement become clearer when candidates have to explain what they know, apply it to a situation and justify their thinking.

In this assessment candidates are given open-ended, role-relevant questions and asked to respond in their own words. Questions can have multiple valid answers or approaches, requiring candidates to connect concepts, apply their knowledge to the given context and explain the reasoning behind their response.

AI evaluates the substance of each answer, scoring it across Conceptual Understanding, Relevance, Accuracy, Depth, Completeness, Reasoning and Clarity. Dimensions can be configured to match the skills being assessed.

The assessment can be used across technical and non-technical roles where deeper understanding and applied thinking matter.

Fraud detection at every layer

Strong assessment signals depend on knowing that the right candidate completed the assessment under the intended conditions.

HirePro verifies candidate identity through ID checks and face matching, while monitoring for impersonation, multiple people, candidate absence, blocked cameras, and other suspicious behaviour throughout the assessment.

Browser activity and test-environment controls help detect tab switching, movement outside the assessment window, screen sharing, screen mirroring, and access to prohibited resources. Violations are captured with timestamps and session evidence, helping hiring teams quickly identify and review suspicious assessment attempts.