How Canadian Banks Should Evaluate AI-Ready Contract Engineers

Canadian banks should evaluate AI-ready contract engineers across four areas: engineering fundamentals, AI fluency, responsible AI judgment, and role-specific AI engineering skills. The strongest candidates can use AI productively, validate its output, protect sensitive data, identify security and model risks, and make sound decisions within a regulated banking environment. Banks should assess these capabilities through structured, role-specific technical interviews. 

At a glance, large Canadian banks should assess four dimensions of AI readiness: 

As Canadian banks rapidly incorporate AI into software development, fraud detection, customer experiences, risk management, and other critical functions, the demand for AI-ready engineering talent is rising. Contract engineers help banks scale talent quickly and bring in specialized skills. 

However, the stakes of a bad hire in banking are much higher compared to other industries. AI already introduces and amplifies risks around cybersecurity, data governance, privacy, bias, and third-party dependencies. Hiring an engineer who lacks the skills or judgment to manage these risks can compound them. 

Canadian banks can’t afford to assess contract engineers solely on their resumes, and they need to look beyond whether an engineer can use AI. They need to understand whether an engineer can use AI effectively without creating technical, security, privacy, or operational risk. This includes the ability to build, evaluate, secure, and responsibly deploy AI-enabled systems.

The stakes are rising too, as the Office of the Superintendent of Financial Institutions’ (OSFI) Guideline E-23 will go into effect on May 1, 2027. It applies to all federally regulated financial institutions operating in Canada and establishes an enterprise-wide, risk-based model risk management framework. 

With Guideline E-23 on the horizon, it’s more important than ever for Canadian banks to accurately assess contract engineers and hire those with the right skills.  

What AI Risks Should Canadian Banks Evaluate When Hiring Engineers?

Canadian banks should evaluate whether engineers can identify and manage data privacy, cybersecurity, model, legal, operational, and third-party risks. The depth of the assessment should reflect the engineer’s access to sensitive data, production systems, customer-facing decisions, and critical banking operations.

In banking, engineers often work with sensitive customer data, financial systems, fraud detection, credit decisions, and other high-impact applications. A solution that appears technically correct can still create serious problems if it exposes sensitive data, relies on poor-quality inputs, produces inconsistent outcomes, or fails. 

OSFI has identified several growing risks as the use of AI at financial institutions rapidly increases:

• Data privacy and security: AI creates risk across the data lifecycle. This includes data privacy, data governance, and data quality.
• Model risk: AI models are more complex and opaque. They’re harder to explain, making it more difficult for banks to provide the right level of explanation to customers, internal stakeholders, and regulators.  
• Legal risk: Banks can face exposure from privacy violations, biased outcomes, or failure to properly disclose and obtain consent for AI use.
• Business risk: Organizations that don’t adopt AI may face financial and competitive pressure. 

The Bank of Canada’s 2026 Financial System Survey found similar risks. Senior risk management experts in the financial sector identified these as the top three AI-related risks to business operations:

• Data quality, bias, or representativeness
• Cybersecurity or data privacy
• Model risk or lack of explainability or interpretability

What Does AI-Ready Mean for a Contract Engineer in Banking?

An AI-ready contract engineer can use AI tools effectively without compromising security, privacy, reliability, or governance. AI readiness combines strong engineering fundamentals, AI fluency, responsible risk judgment, and specialized AI engineering skills when the role requires them.

To assess AI readiness, banks should look at three areas:

• AI Fluency and Output Validation
• Responsible AI and Risk Judgment
• Role-Specific AI Engineering Skills

AI Fluency and Output Validation

AI fluency is the ability to use AI tools productively while understanding their limitations. An AI-ready contract engineer should be able to use AI to accelerate routine work, understand unfamiliar codebases, generate code, debug issues, and explore solutions. 

Banks should look for engineers who demonstrate that they can:

• Use AI tools productively.
• Write effective prompts.
• Review, test, and validate AI output.
• Identify hallucinations and incorrect assumptions.
• Select the appropriate models.

AI Responsibility and Risk Judgment 

Engineers who work with AI need to understand the risks that AI creates when embedded in products and workflows. They should recognize:

• Privacy risks
• Bias and fairness concerns
• Security vulnerabilities
• Hallucinations and model failure modes
• Data leakage
• Prompt injection
• Model drift
• Explainability requirements

It’s critical to know when an engineering decision has implications beyond the codebase and when to involve the appropriate security, privacy, legal, risk, or governance stakeholders.

Role-Specific AI Engineering Skills

For engineering roles that involve building AI models, products, or systems, contractors need deeper technical capabilities that reflect the specific application and risk profile of the work. Depending on the role, this may include:

• Building agentic workflows
• Fine-tuning
• AI security
• Model evaluation
• Retrieval-augmented generation (RAG) and retrieval systems
• Data pipelines and data quality
• Observability and monitoring
• Model routing
• Performance and cost tradeoffs

How Should Canadian Banks Evaluate AI- Ready Contract Engineers?

Canadian banks should use a five-step process: define the role’s AI competencies, assess its risk profile, create a realistic technical interview, evaluate the candidate’s process and judgment, and score every candidate against a consistent technical bar.

Define the AI Competencies Required for the Role

What AI readiness means differs by role. Before designing interview content and assessment formats, you need to define the skills that a contractor needs to be successful. This should be based on their responsibilities, the systems they’ll work on, and the types of AI tools and workflows they’ll use. 

Assess the Role’s Risk Profile

Consider the contractor’s level of system access, the sensitivity of the data they’ll work with, their use of third-party models or APIs, and the potential impact on customers, employees, or critical operations. 

A contractor improving internal AI tools shouldn’t be evaluated in the same way as one working on an AI-assisted fraud workflow. For the latter, evaluations should place greater emphasis on data quality, observability, failure modes, auditability, and judgment. 

Develop a Realistic, Role-Specific Technical Interview

Once you’ve identified the competencies and risk factors that matter for a contract role, the next step is to design an assessment that can accurately measure them. Banks need to look beyond whether a candidate can produce a technically correct solution. What’s more important is understanding how the candidate approaches problems, how they use AI, and whether they can recognize and manage the risks of working with AI. 

Interview content should reflect the type of work that contractors will do on the job. If they’ll be working with multi-file codebases, incomplete documentation, third-party integrations, or an ambiguous issue, those aspects should be incorporated into the problems that you present to candidates. 

Here are best practices for designing interviews in the AI era:

• Evaluate judgment, not just output. AI can produce working solutions and correct answers without the candidate fully understanding the problem or result. That’s why banks should avoid relying solely on take-home assessments or output-based assessments like code tests. Additionally, a technically correct solution can still introduce risk through security vulnerabilities, privacy problems, unnecessary model costs, and data leakage. Instead of only looking at the candidate’s solution, look at how they got there. Dig into their approach, how they verified AI-generated output, tradeoffs made, and privacy and security concerns. 
• Give candidates access to AI tools. The most effective interviews mirror the engineering environment that the candidate will be working in. If AI is part of the job, interviews that prohibit AI use won’t accurately reflect how the candidate will actually perform on the job. Despite this, 62% of organizations still prohibit AI use in technical interviews. 
• Use human + AI interviews. A human + AI interview gives candidates access to authorized AI tools while a human interviewer evaluates how they use those tools, validate outputs, identify risks, explain tradeoffs, and make technical decisions in real time.

Evaluate Candidates Against a Scoring Rubric

A structured scoring rubric establishes criteria that every candidate should be evaluated against. The rubric should define the competencies required for the role and clearly describe what strong performance looks like for each competency. When interviewers use the same scoring rubric, it reduces bias and subjectivity.

How Can Large Canadian Banks Maintain a Consistent Technical Bar Across IT Service Providers?

Large banks often source engineering talent through internal recruiting, multiple IT service providers (ITSPs), and staffing firms. Applying the same role-specific assessments, scoring rubrics, and quality standards across these channels creates comparable hiring evidence and reduces variations in contractor quality across providers and business units.

We’ve observed that there’s a gap between full-time and ITSP contractor talent in FinServ. While 66% of FinServ employees meet the Tier-1 bank bar, only 55% of ITSP contractors do. 

To close that gap, banks should use the same assessments, interview content, and scoring rubric across the board. This ensures that candidates are held to the same standard. 

For organizations working with multiple IT service providers, achieving consistency in talent quality also involves tracking outcomes by provider. By measuring things like pass rate, placements, and time-in-process, you’ll be able to hold ITSPs accountable and make more informed decisions about where to source contract engineering talent. 

A technical interview does not establish regulatory compliance. However, it can provide consistent evidence that engineers have the technical skills and risk judgment required to work within a bank’s AI governance framework.

How Karat Helps Canadian Banks Evaluate AI-Ready Contract Engineers at Scale

In banking and financial services, being AI-ready doesn’t just mean being able to produce working code with AI assistance. Engineers work in an environment with serious data, security, model, operational, and third-party risks. That means they also need to be able to validate AI output, protect sensitive data, identify failure modes, weigh tradeoffs, and make sound technical decisions. 

The most effective way to evaluate these skills is through human + AI technical interviews that mirror the work contract engineers will actually do. By defining clear competencies, allowing candidates to use AI, having a human lead live interviews, and using a standardized scoring rubric, banks can get a reliable, accurate hiring signal. 

Karat helps financial institutions evaluate AI-ready technical talent at scale. 

• Karat NextGen, the first human-led, AI-enabled evaluation solution, allows candidates to demonstrate how they work with AI while experienced Interview Engineers evaluate their problem-solving and decision-making skills.
• Karat Partner Talent Solutions measures contractor quality, giving organizations insight into ITSP performance and making it easier to uphold the same technical bar across contract and full-time talent. 

Contact us to see how our solutions can improve the quality of your contract engineering workforce. 

FAQs

What makes a contract engineer AI-ready?

An AI-ready contract engineer has strong engineering fundamentals, AI fluency, and sound technical judgment. They can use AI tools effectively, validate AI-generated output, understand AI’s limitations, and identify security, privacy, model, and operational risks. 

What AI skills should Canadian banks test in contract engineers?

Canadian banks should test for a combination of engineering fundamentals, AI fluency, and risk management. For AI engineering roles, they should look for more advanced skills like fine-tuning, RAG, and model evaluation.

How do you assess AI skills in a software engineer?

The most reliable way to assess AI skills is through human + AI interviews. This assessment format gives candidates access to AI tools in a realistic environment, and uses a human interviewer who can ask follow-up questions and evaluate the candidate’s reasoning and judgment in real time. 

How should Canadian banks assess contractors working on high-risk AI systems?

Banks should increase the depth of assessment for roles involving sensitive data, production access, customer impact, critical operations, or high-impact models. Interviews should evaluate data quality, privacy, security, explainability, monitoring, auditability, human oversight, and fallback planning.

How should technical interviews assess AI readiness?

Technical interviews should mirror the AI-enabled environment in which the engineer will work. They should allow candidates to use authorized AI tools during the interview, and evaluate how candidates work with those tools, validate AI output, identify risks, and make decisions.  

How can banks evaluate contractor quality consistently across IT service providers?

Banks can apply the same technical bar, interview content, and scoring rubric across providers. Tracking results for each IT service provider also helps banks identify differences in contractor quality, and determine which providers consistently meet the bar and which need recalibration.

Should software engineers be allowed to use AI during technical interviews?
Yes, if engineers will use AI in the role. Candidates should use authorized tools under clear data-handling rules, while a human interviewer evaluates how they use AI, validate its output, identify risks, and explain their decisions.

Does an AI-readiness interview demonstrate OSFI compliance?
No. A technical interview is one talent-quality control and does not establish regulatory compliance. It can help banks determine whether engineers have the technical skills, risk awareness, and judgment needed to work within an OSFI-aligned governance framework.