New opportunity

AI Native Software Engineer

Accenture · Bengaluru, India

About this role

Accenture seeks an AI Native Software Engineer in Bengaluru to build and ship production-ready software with AI integrated into the engineering lifecycle. The role focuses on developing AI-enabled ServiceNow features while maintaining quality, compliance and secure engineering practices.

Responsibilities

Develop features using ServiceNow Now Assist, Virtual Agent and AI Search, alongside App Engine Studio, Flow Designer and IntegrationHub.
Implement agentic patterns for workflow automation and developer assistance. Write prompts and evaluators, and contribute to RAG strategies and testing frameworks.
Work with architects and product owners in agile pods, participating in sprints and code reviews. Apply Responsible AI principles and improve how software is built and operated.

Qualifications and skills:

ServiceNow is a required skill. Strong JavaScript, REST API and ServiceNow platform development fundamentals are sought, along with familiarity with ServiceNow App Engine, Flow Designer, IntegrationHub and Scoped Applications.
Understand LLMs, prompt engineering and evaluation metrics; know Virtual Agent, AI Search tuning and Now Assist configuration. The posting also calls for exposure to ServiceNow data models, UI policies, Business Rules and client scripts; awareness of SOAP/REST integrations, MID Server, OAuth and SAML; and a basic understanding of RAG, vector search and orchestration frameworks.
Bring curiosity, collaboration, communication, problem-solving, attention to detail and adaptability in client-focused environments.

The posting lists 2–5 years of experience in its header and separately requires a minimum of 3 years. It specifies 15 years of full-time education. This is a full-time position.

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

Skills for this role

ServiceNowNow AssistVirtual AgentAI SearchApp Engine StudioFlow DesignerIntegrationHubJavaScriptREST APIsScoped ApplicationsLarge Language ModelsPrompt EngineeringAI EvaluationRAGVector SearchSOAPMID ServerOAuthSAMLServiceNow Data ModelsUI PoliciesBusiness RulesClient ScriptsWorkflow AutomationResponsible AIAgile

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

Ship AI-enabled ServiceNow features
The role calls for developing Now Assist, Virtual Agent and AI Search features using App Engine Studio, Flow Designer and IntegrationHub. Prepare to explain how a feature moves from platform configuration through validation to a production-ready release.
Build reliable platform workflows and integrations
ServiceNow is the required skill; the posting also seeks JavaScript, REST API and platform-development fundamentals, plus familiarity with Scoped Applications. Prepare a concrete example of workflow logic or an integration, including how you handled errors and testing.
Evaluate agentic and LLM behavior
The stated duties include implementing agentic patterns for workflow automation and developer assistance, authoring prompts and evaluators, and contributing to RAG strategies and testing frameworks. Prepare to describe what an evaluator would measure and how its results would guide changes.
Maintain quality, security and collaboration
The posting calls for production-ready software, Responsible AI and secure engineering practices, alongside sprint work and code reviews with architects and product owners. Prepare an example showing how review or testing improved an implementation.

Likely challenges to think through

Balancing AI usefulness with predictable behavior
A likely challenge, not a stated employer problem, is that helpful AI responses may still be inconsistent or incorrect. Prepare to discuss test cases, evaluation metrics and boundaries for prompts or agentic workflows before release.
Connecting AI features to governed platform data
A plausible difficulty is making AI Search or RAG useful without mishandling ServiceNow data or access controls. Prepare to explain how you would check data sources, permissions and response quality; the posting names RAG, vector search and secure engineering but does not prescribe an architecture.
Moving quickly without weakening release quality
Sprint delivery and code reviews can create a trade-off between iteration speed and the posting's quality and compliance goals. Prepare to explain how you would scope a change, test it and surface risks to architects and product owners.

Practice your answers

Technical practice

How would you structure an AI-enabled feature in a ServiceNow scoped application?

What to coverPreparation suggestion: Explain how the data model, application scope and platform components would fit together. Discuss maintainability and access boundaries without assuming a particular client implementation.

When would you use a Business Rule, client script or UI policy?

What to coverPreparation suggestion: Compare server-side logic with client-side behaviour and explain where each is appropriate. Describe how you would avoid conflicting logic and test the resulting behaviour.

How would you automate a ServiceNow workflow using Flow Designer and IntegrationHub?

What to coverPreparation suggestion: Walk through a plausible workflow, including its trigger, actions, external connection and failure handling. Explain how you would decide what belongs in a flow versus custom JavaScript.

How would you configure and validate a Now Assist feature?

What to coverPreparation suggestion: Describe how you would define the feature's intended task and check the usefulness and accuracy of its outputs. Address access to underlying data and what should happen when the output is unsuitable.

How would you design a Virtual Agent interaction?

What to coverPreparation suggestion: Explain how you would define the user's goal, conversation paths and handoff or fallback behaviour. Describe how you would test whether the interaction completes the intended task.

How would you tune AI Search, and when might RAG help?

What to coverPreparation suggestion: Distinguish search relevance from generating an answer grounded in retrieved content. Discuss basic retrieval checks, including whether the right content is available and whether an answer reflects it.

How would you write and evaluate prompts for a ServiceNow AI feature?

What to coverPreparation suggestion: Explain how you would specify the task, context, output constraints and representative test cases. Compare possible evaluation measures such as task success, accuracy and unsafe or unsupported responses.

Where could an agentic pattern help with workflow automation or developer assistance?

What to coverPreparation suggestion: Describe a bounded task an agent could perform and what tools, permissions and checkpoints it would need. Weigh automation benefits against incorrect actions and the need for human review.

How would you approach a ServiceNow integration that requires authentication?

What to coverPreparation suggestion: Compare REST and SOAP at a high level, and explain when a MID Server might be relevant. Discuss how you would investigate an authentication or connectivity failure involving OAuth or SAML without exposing credentials.

What checks would you make before shipping an AI-enabled ServiceNow feature?

What to coverPreparation suggestion: Cover functional testing, AI-output evaluation, secure engineering and Responsible AI risks. Explain how code review and repeatable tests could help maintain quality and compliance as the feature changes.

Behavioural practice

Tell me about collaborating with product and architecture stakeholders on a feature.

What to coverPreparation suggestion: Use a real situation to explain the differing priorities, your actions to clarify decisions and the result. Highlight how you communicated trade-offs during iterative delivery.

Describe a code review that changed your approach.

What to coverPreparation suggestion: Give the situation, the feedback or disagreement, the action you took and the result. Focus on how the exchange improved quality or maintainability rather than on winning an argument.

Tell me about diagnosing a difficult software defect.

What to coverPreparation suggestion: Describe the symptoms and constraints, the steps you took to isolate the cause and the result. Show attention to detail, including how you checked that the fix worked.

Describe a time you had to learn an unfamiliar technology quickly.

What to coverPreparation suggestion: Explain the situation, how you identified what you needed to learn, what you applied and the result. Keep the example tied to delivering useful work rather than listing topics studied.

Tell me about adapting when a client-facing requirement changed.

What to coverPreparation suggestion: Outline the original goal, what changed, how you reprioritised or communicated the impact and the result. Explain how you protected quality while responding to the change.

Sharpen your knowledge

Topics to prepare

ServiceNow application architecture
The posting calls for ServiceNow platform development, Scoped Applications, data models, UI policies, Business Rules and client scripts. Revise by sketching a scoped application's data flow and explaining where each rule type belongs.
ServiceNow AI capabilities
The role names Now Assist, Virtual Agent and AI Search, including configuration and search tuning. Revise by comparing what each would do in one service workflow and how you would check whether it helps users.
LLMs, evaluation and retrieval
The posting asks for LLM and evaluation-metric understanding, plus basic RAG, vector search and orchestration concepts. Revise by diagramming a retrieval-assisted answer and identifying measures for answer quality and retrieval relevance.
Integration and authentication patterns
The posting seeks REST API fundamentals and awareness of SOAP/REST, MID Server, OAuth and SAML. Revise by explaining when each applies in a ServiceNow integration and where authentication or connectivity can fail.
Responsible AI and secure delivery
The role explicitly calls for Responsible AI, secure engineering, quality and compliance. Revise by walking through an AI-enabled workflow and identifying data-exposure risks, failure modes and checks before release.

Skills to revise

Implement ServiceNow logic
Practice writing a small JavaScript Business Rule and client script for a sample record, then test expected and invalid inputs. This exercises the posting's JavaScript and ServiceNow development fundamentals; using a sandbox, if available, is a preparation suggestion.
Build a scoped workflow
If you have platform access, create a small Scoped Application in App Engine Studio with a Flow Designer workflow and an IntegrationHub step; otherwise, map the steps and inputs on paper. These tools are named in the responsibilities.
Configure an AI-enabled interaction
In a practice environment, outline or configure a Virtual Agent interaction that uses an AI Search result and identify where Now Assist would add value. Check the result against a few representative queries; the posting names all three capabilities.
Write prompts and evaluators
Draft prompts for one workflow-automation or developer-assistance task, then create a small test set and scoring rubric for useful, incorrect and unsafe outputs. The posting explicitly asks for prompts, evaluators, agentic patterns and testing frameworks.
Exercise an integration review
Practice tracing a ServiceNow-to-REST request: specify inputs, authentication, error handling and a test for failed responses. Explain how you would discuss the change in a code review; REST APIs, secure practices and code reviews appear in the posting.

Your preparation checklist

Prepare a ServiceNow feature walkthrough
Bring a diagram or, if no relevant project is available, a clearly labeled design example for an AI-enabled feature using Now Assist, Virtual Agent or AI Search. Explain where App Engine Studio, Flow Designer and IntegrationHub would fit.
Work through a platform design exercise
Sketch a scoped application with a data model, Business Rule, client script and UI policy. Be ready to explain JavaScript choices, REST API behavior and how you would test the workflow.
Map an integration and its security controls
Prepare a one-page integration design covering REST or SOAP, when a MID Server might be needed, and how OAuth or SAML would apply. Mark assumptions rather than presenting the design as an Accenture implementation.
Draft an AI evaluation plan
Write a sample prompt and evaluator for a ServiceNow workflow. Define measurable quality checks, test cases and failure handling; explain where RAG, vector search or agentic orchestration could be useful and where they may not be.
Prepare a production-readiness review
Create a short checklist for secure engineering, Responsible AI, compliance, testing and code review of the proposed feature. Connect each check to a concrete risk or validation step.
Bring collaboration evidence and role questions
Prepare a concise example of working through feedback with architects or product owners in sprints. Ask which ServiceNow AI features are priorities, how quality is evaluated, and how the posting’s 2–5-year header relates to its three-year minimum and entry-level JD label.

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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