New opportunity

Responsible AI Engineer

Accenture · Singapore

About this role

Accenture seeks a Responsible AI Engineer for its Global Responsible AI team in Singapore. The role develops and deploys AI solutions for clients while identifying and mitigating ethical, technical and regulatory risks. The posting’s structured experience requirement states 120–144 months (10–12 years).

Responsibilities

Develop AI models and solutions using responsible practices; evaluate bias, fairness, robustness and explainability, and mitigate identified risks.
Optimize and maintain existing AI systems for performance and adherence to ethical standards. Work across data preparation, design, coding, testing, deployment and support.
Identify and correct bias in datasets and models; document development processes, decisions and methodologies.
Contribute to responsible AI guidelines, best practices and technical training; stay current with AI developments and ethical considerations. Support client sales conversations and proposals.

Qualifications

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science or a related technical field, or equivalent work experience of at least 12 years. An associate’s degree requires at least 6 years of work experience.
At least 3 years developing and deploying machine-learning models and AI solutions, with experience in at least two listed areas: statistical and forecasting methods; deep learning and neural networks; NLP, text mining, computer vision or image recognition; and machine-learning visualization tools.
At least 5 years of experience in the listed technical areas, including programming in Python, R or Java; AI frameworks such as Scikit-learn, TensorFlow or PyTorch; API development with Django, Flask or FastAPI; version control; databases and data processing; cloud AI services; and complex-dataset preprocessing and analysis.
At least 1 year addressing model fairness, transparency, explainability, robustness, soundness and privacy, preferably in a client-facing environment. Demonstrated prompt-injection and jailbreak testing experience is critical, as is model vulnerability assessment; the posting also lists Promptfoo, Rebuff and PyRIT.
Strong analytical, problem-solving, collaboration, communication, documentation, presentation and project-prioritization skills, with a commitment to ethical AI development.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Responsible AIMachine LearningAI FairnessBias DetectionExplainabilityAI RobustnessAI PrivacyPrompt Injection TestingJailbreak TestingModel Vulnerability AssessmentPromptfooRebuffPyRITPythonRJavaScikit-learnTensorFlowPyTorchDjangoFlaskFastAPIGitHubBitbucketAWSAzureGoogle CloudData PreprocessingStatistical AnalysisRegression Modeling nlp text mining data visualization etc?

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Build and deploy responsible AI solutions
The role develops AI models for client business challenges across data preparation, design, coding, testing, deployment and support, with responsible practices built into that lifecycle.
Evaluate and mitigate model risks
The posting calls for assessing bias, fairness, transparency, explainability, robustness, soundness and privacy, then mitigating identified risks. It also identifies prompt-injection and jailbreak testing, and model vulnerability assessment, as critical experience.
Improve existing systems without losing safeguards
Optimizing and maintaining deployed AI systems for performance and adherence to ethical standards is an explicit duty, alongside identifying and correcting bias in datasets and models.
Make responsible AI work usable by others
The role documents development decisions and methods, contributes to internal guidelines and technical training, and supports client sales conversations and proposals.

Likely challenges to think through

Balancing performance with risk mitigation
Likely preparation challenge, not a confirmed employer problem: a model improvement could affect fairness, robustness or explainability. Prepare to explain how you would compare performance and risk evidence, choose mitigations and record the trade-off.
Testing vulnerabilities in realistic AI systems
Likely preparation challenge, not a confirmed employer problem: prompt-injection and jailbreak findings may depend on context and be difficult to reproduce or prioritize. Prepare to describe a test method, how you would document a finding, and how you would check a mitigation.
Translating technical findings for clients
Likely preparation challenge, not a confirmed employer problem: clients and technical colleagues may need different levels of detail while decisions must remain transparent. Prepare a concise way to communicate a model risk, its limitations and the proposed response without overstating certainty.

Practice your answers

Technical practice

How would you choose an AI modelling approach for a difficult client problem?

What to coverThe posting names statistical methods, deep learning, and language or vision applications as relevant areas. Suggested preparation: explain how the problem, available data, evaluation criteria, and responsible-AI risks would shape your choice and its trade-offs.

How would you find and address bias in a complex dataset before training?

What to coverThe role includes preprocessing complex datasets and correcting dataset bias. Suggested preparation: discuss representation, missingness, labels, and subgroup analysis, then explain how you would check whether a correction introduced new problems.

How would you evaluate fairness in a deployed model?

What to coverThe posting explicitly calls for evaluating fairness and mitigating identified risks. Suggested preparation: explain how you would select relevant groups and measures for the use case, investigate differences, and weigh fairness against model performance.

How would you assess a model's explainability, transparency, and soundness?

What to coverThese are responsible-AI dimensions named in the posting. Suggested preparation: distinguish an explanation of an individual output from documentation of how the system was built, and describe how you would test whether explanations faithfully reflect model behaviour.

How would you test robustness and privacy risks in an AI solution?

What to coverThe role calls for attention to robustness and privacy alongside other responsible-AI risks. Suggested preparation: describe plausible input variations and data-exposure scenarios, how you would measure their effects, and how mitigation might affect utility.

How would you design prompt-injection and jailbreak tests?

What to coverThe posting identifies demonstrated prompt-injection and jailbreak testing experience as critical. Suggested preparation: explain how you would define boundaries, construct varied adversarial inputs, assess outcomes, and retest after mitigations without treating a passing test set as proof of safety.

How would you conduct and report a model vulnerability assessment?

What to coverModel vulnerability assessment is explicitly identified as critical; the posting also lists Promptfoo, Rebuff, and PyRIT. Suggested preparation: describe a reproducible assessment plan, severity and impact criteria, and how you would record findings and verify fixes; discuss named tools only where relevant to the approach.

How would you take an AI solution from development to deployment and support?

What to coverThe stated lifecycle spans data preparation, design, coding, testing, deployment, and support, with API development, version control, databases, and cloud AI services among the listed technical areas. Suggested preparation: explain the handoffs and checks you would use to keep a solution reproducible and responsible across those stages.

How would you improve an existing AI system without weakening its ethical safeguards?

What to coverOptimizing and maintaining existing systems for performance and adherence to ethical standards is a stated duty. Suggested preparation: describe how you would establish a baseline, diagnose a performance issue, compare changes across quality and responsible-AI measures, and decide whether to release or revert.

What would you document when a responsible-AI risk leads to a design change?

What to coverThe role requires a transparent record of development decisions and methodologies. Suggested preparation: cover the risk and evidence, alternatives considered, decision rationale, tests, remaining limitations, and information needed by future maintainers.

Behavioural practice

Tell me about a time you explained an AI risk to a client or non-technical stakeholder.

What to coverThe posting emphasizes client communication and presentations for technical and non-technical audiences. Suggested preparation: use a real situation, your actions to make the risk and trade-offs understandable, and the resulting decision or feedback.

Tell me about a time you worked with others to resolve a difficult modelling or responsible-AI issue.

What to coverThe role calls for collaborative problem-solving with colleagues. Suggested preparation: describe the situation, your specific contribution and how you handled differing views, then the result and what the team learned.

Tell me about a time you balanced competing project priorities.

What to coverManaging multiple projects and priorities is a stated professional skill. Suggested preparation: explain the constraints, how you communicated and made trade-offs, and the outcome, including any work you deferred.

Tell me about a time you turned a technical lesson into guidance or training for others.

What to coverThe work includes contributing to responsible-AI guidelines, best practices, and technical training. Suggested preparation: give a real situation, explain how you adapted the material to its audience, and describe evidence of its usefulness.

Tell me about a time you helped define the scope of an AI solution before delivery.

What to coverThe posting includes support for client sales conversations and proposals. Suggested preparation: if you have a relevant example, describe the situation, how you clarified feasibility and responsible-AI risks, and what resulted; do not imply you owned a proposal if you did not.

Sharpen your knowledge

Topics to prepare

Responsible AI dimensions and trade-offs
Suggested revision exercise: For a hypothetical model, define how you would assess fairness, transparency, explainability, robustness, soundness and privacy, then explain where improving one measure could affect another. Relevance: The posting explicitly calls for experience assessing these dimensions and mitigating risks.
Prompt-injection and jailbreak threat assessment
Suggested revision exercise: Map how an attacker-controlled input could redirect an AI system, and distinguish an attempted injection, a successful jailbreak and the underlying model vulnerability. Relevance: Demonstrated prompt-injection and jailbreak testing and model vulnerability assessment are described as critical.
Bias across data and model decisions
Suggested revision exercise: Trace how sampling, preprocessing and model outputs could produce different outcomes across groups; identify what evidence would justify a correction. Relevance: The role includes identifying and correcting bias in datasets and AI models.
Applied modelling foundations
Suggested revision exercise: Review two areas from the posting’s list—for example, regression and time-series forecasting, or neural networks and NLP—and explain how you would evaluate a model built with each. Relevance: The posting specifies experience in at least two listed modelling or visualization areas, not every area.
Responsible AI throughout the lifecycle
Suggested revision exercise: Sketch decision points for risk assessment from data preparation through design, testing, deployment and support, including when to revisit an existing system. Relevance: The role spans the full AI lifecycle and includes maintaining systems for performance and adherence to ethical standards.

Skills to revise

Run and document adversarial tests
Suggested revision exercise: In a safe test setup, write prompt-injection and jailbreak test cases, record expected versus observed behavior, and propose mitigations and retests. If available, practise with a tool listed in the posting—Promptfoo, Rebuff or PyRIT. Relevance: Testing experience, vulnerability assessment and these tools appear explicitly in the qualifications.
Audit a complex dataset for bias
Suggested revision exercise: Use a sample dataset to document preprocessing choices, missing-data patterns and group-level outcome differences; test whether a proposed correction changes results. Relevance: The posting calls for complex-dataset preprocessing and analysis and correction of dataset and model bias.
Build and evaluate an AI model
Suggested revision exercise: Implement a small model in Python, R or Java using a listed framework such as Scikit-learn, TensorFlow or PyTorch; report performance alongside a relevant fairness or robustness check. Relevance: Programming, AI frameworks, model development and responsible evaluation are stated requirements.
Expose and maintain a model through an API
Suggested revision exercise: Wrap a model in a small API using Django, Flask or FastAPI, add tests for invalid inputs, and describe how you would monitor performance and address a discovered risk after deployment. Relevance: The posting lists rapid API development and responsibility for testing, deployment, support and optimization.
Work with versioned data and cloud components
Suggested revision exercise: Version code and an analysis record, outline how data would move between a database, preprocessing step and an AI service on AWS, Azure or Google Cloud, and identify access or privacy decisions to review. Relevance: Version control, databases, data processing and cloud AI services are listed; the workflow is a suggested way to revise them, not a prescribed architecture.
Communicate risk decisions to clients and colleagues
Suggested revision exercise: Prepare a one-page technical decision record and a short non-technical explanation of a model risk, mitigation, remaining limitation and next test. Relevance: The role requires transparent documentation, presentations, collaboration, and support for client conversations and proposals.

Your preparation checklist

Build a requirements evidence map
Prepare a one-page map of the posting’s structured 120–144 months of experience, education or work-experience alternatives, and stated 3-year ML, 5-year technical and 1-year responsible-AI experience requirements. Link each area to specific examples or note where evidence is unavailable.
Prepare an end-to-end AI project walkthrough
Choose a project you can discuss and outline the business problem, data preparation, modelling choices, testing, deployment and support. Identify evidence for at least two of the listed ML areas and for the programming, frameworks, APIs, version control, data processing and cloud tools you actually used.
Document a responsible-AI assessment
Prepare an example showing how you evaluated relevant dimensions such as bias, fairness, explainability, robustness or privacy. Include the measures chosen, risks found, mitigations, trade-offs and a transparent record of decisions; distinguish work performed from checks you would propose.
Show prompt-attack and vulnerability testing
Prepare a concrete account of prompt-injection and jailbreak tests and a model vulnerability assessment, including test cases, observed behaviour, fixes and retesting. The posting calls this experience critical and names Promptfoo, Rebuff and PyRIT; discuss those tools only where you have used them.
Rehearse a lifecycle design exercise
As interview practice, sketch how you would design or improve a client AI system from data preparation through deployment and maintenance. Explain where responsible-AI checks, performance optimization, documentation and ongoing support would fit; present this as your proposed approach, not Accenture’s prescribed process.
Practice a client-facing explanation
Prepare a brief technical and non-technical explanation of an AI risk, its mitigation and its remaining limitations. Rehearse how you would use it in a client discussion or proposal, and draft a short example of guidance or training you could contribute; these reflect duties in the posting.
Bring targeted questions for the team
Write questions about the AI systems this role supports, how the team prioritizes responsible-AI risks with clients, what evidence it expects from model vulnerability testing, and how engineers contribute to guidelines and training. Use the answers to clarify the role rather than assume a particular process.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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