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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 full-time role is also described as a ServiceNow AI Developer and AI Native Software Engineering Senior Analyst.

Responsibilities

  • Develop AI-enabled ServiceNow features using 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 retrieval-augmented generation (RAG) strategies and testing frameworks.
  • Develop and validate solutions, maintain quality and compliance, and improve how software is built and operated. Work with architects and product owners in agile sprints and code reviews, applying Responsible AI principles and secure engineering practices.

Qualifications

  • A minimum of 3 years of experience and 15 years of full-time education are required. The listing also labels the role as requiring 2–5 years of experience.
  • Strong fundamentals in JavaScript, REST APIs and ServiceNow platform development; familiarity with ServiceNow App Engine, Flow Designer, IntegrationHub and Scoped Applications.
  • Understanding of LLMs, prompt engineering and evaluation metrics; knowledge of Virtual Agent, AI Search tuning and Now Assist configuration. 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.
  • Collaboration, communication, problem-solving, attention to detail, adaptability and a continuous-learning mindset are sought.

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 APIsServiceNow platform developmentScoped ApplicationsLarge Language Models (LLMs)Prompt EngineeringAI EvaluationRetrieval-Augmented Generation (RAG)Vector SearchWorkflow AutomationServiceNow data modelsUI PoliciesBusiness RulesClient ScriptsSOAPMID ServerOAuthSAMLResponsible AISecure EngineeringAgile DevelopmentCommunicationCollaboration

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Deliver AI-enabled ServiceNow features

    The role calls for developing features with Now Assist, Virtual Agent and AI Search using App Engine Studio, Flow Designer and IntegrationHub. ServiceNow platform development, JavaScript and REST APIs are core foundations.

  • Build and evaluate AI-native workflows

    The posting specifies agentic patterns for workflow automation and developer assistance, plus prompts, evaluators, RAG strategies and testing frameworks.

  • Ship production-ready, responsible software

    The role emphasizes developing and validating solutions, maintaining quality and compliance, and applying Responsible AI principles and secure engineering practices.

  • Work across agile delivery

    The engineer is expected to collaborate with architects and product owners in agile pods, participate in sprints and code reviews, and improve how software is built and operated.

Likely challenges to think through

  • Balancing AI usefulness with dependable behavior

    A likely challenge is making Now Assist, Virtual Agent or AI Search useful without accepting unreliable outputs. Prepare to explain how prompts, evaluation metrics and testing could expose failures and guide improvements; the posting does not specify a particular evaluation method.

  • Connecting automation without weakening security

    A likely trade-off is workflow convenience versus safe access to platform data and external systems. Prepare to discuss how you would reason about ServiceNow data models, IntegrationHub, REST or SOAP integrations, and authentication mechanisms such as OAuth or SAML.

  • Maintaining quality while iterating in sprints

    AI-enabled features may need repeated tuning while still meeting production quality, compliance and code-review expectations. Prepare to describe how you would validate changes, communicate trade-offs with architects and product owners, and keep secure engineering practices in view.

03

Practice your answers

Technical practice

Suggested practice: How would you decide where JavaScript logic belongs in a ServiceNow application?

What to coverThe posting calls for JavaScript fundamentals and exposure to Business Rules, client scripts, UI policies, and ServiceNow data models. As preparation, explain a client-side versus server-side decision and how you would test the resulting behavior.

Suggested practice: How would you structure an AI-enabled feature in a Scoped Application?

What to coverThe role names ServiceNow platform development, App Engine Studio, and Scoped Applications. As preparation, describe how you would model the data and keep the feature maintainable within its application scope.

Suggested practice: When would you use Flow Designer and IntegrationHub for workflow automation?

What to coverBoth tools are named for developing AI-enabled ServiceNow features. As preparation, walk through a plausible workflow and explain where you would handle failures, retries, and human review.

Suggested practice: How would you configure and assess a Now Assist feature?

What to coverThe posting names Now Assist configuration, prompt authoring, and evaluators. As preparation, explain how you would define the intended task, test prompts against representative cases, and check outputs before release.

Suggested practice: How would you design a Virtual Agent interaction?

What to coverVirtual Agent is a stated area of knowledge and feature development. As preparation, describe how you would keep the interaction clear, handle an unsupported request, and check whether it completes the intended task.

Suggested practice: How would you investigate poor AI Search results?

What to coverThe role specifically mentions AI Search tuning. As preparation, discuss checking the query, available content, and result relevance before changing the search configuration; explain how you would compare results afterward.

Suggested practice: What would you consider when contributing to a RAG strategy?

What to coverThe posting calls for basic RAG and vector-search understanding and contributions to RAG strategies. As preparation, explain retrieval, context selection, and grounded answers, including how you would detect an answer unsupported by retrieved material.

Suggested practice: How would you assess an agentic workflow before using it for automation?

What to coverImplementing agentic patterns for workflow automation and developer assistance is a stated responsibility. As preparation, describe task boundaries, permitted actions, validation, and when a person should remain in control.

Suggested practice: How would you evaluate an AI-enabled feature for production use?

What to coverThe role includes evaluators, testing frameworks, quality, compliance, Responsible AI, and secure engineering. As preparation, propose test cases and evaluation measures tied to the feature’s purpose, including failure cases and sensitive-data handling.

Suggested practice: How would you approach an external ServiceNow integration?

What to coverThe posting names REST APIs and awareness of SOAP/REST patterns, MID Server, OAuth, and SAML. As preparation, explain how you would choose an integration pattern, handle authentication and errors, and verify the data exchanged.

Behavioural practice

Suggested practice: Tell me about collaborating across product and architecture roles

What to coverThe posting describes agile pods, architects, product owners, and sprints. Use a real situation, your actions to resolve a requirement or design question, and the resulting decision or delivery outcome.

Suggested practice: Describe a code review that improved quality

What to coverCode reviews and maintaining quality are stated parts of the role. Give a real example of feedback you gave or received, the change you made, and its result.

Suggested practice: Tell me about a difficult software problem you diagnosed

What to coverThe role seeks problem-solving and attention to detail. Describe the symptoms and context, how you investigated rather than guessed, and what the resolution showed.

Suggested practice: Describe a time you raised a security or responsible-use concern

What to coverSecure engineering and Responsible AI are explicit responsibilities. Use a real situation, the concern you identified, how you communicated or addressed it, and the outcome; do not claim experience you do not have.

Suggested practice: Tell me about adapting while learning an unfamiliar capability

What to coverThe posting emphasizes adaptability, curiosity, and continuous learning in a client-focused environment. Give a real situation, what you did to learn and adjust, and the effect on the work.

02

Sharpen your knowledge

Topics to prepare

  • ServiceNow application structure

    Map a sample request from a ServiceNow data model through a scoped application, UI policy, client script and Business Rule. Explain which logic runs in the browser versus on the server. The posting explicitly calls for ServiceNow development fundamentals and exposure to these components.

  • ServiceNow AI feature choices

    For a support-request scenario, compare when Now Assist, Virtual Agent and AI Search would be useful, including what AI Search tuning might change. These are the AI-enabled features named in the role.

  • LLMs, RAG and evaluation

    Draw a basic retrieval-augmented generation flow, then propose a small set of test questions and measures for answer quality and retrieval relevance. The posting asks for LLM and evaluation understanding and basic knowledge of RAG and vector search.

  • Agentic workflow boundaries

    Diagram a workflow in which an AI step proposes an action and a controlled step validates it before execution; identify failure and escalation points. This prepares for the stated work on agentic patterns for workflow automation and developer assistance.

  • Integration and authentication concepts

    Explain how a ServiceNow integration might use REST or SOAP, when a MID Server could be relevant, and the distinct purposes of OAuth and SAML. The listing requests awareness of these integration patterns and mechanisms.

  • Responsible AI and secure delivery

    Review a hypothetical AI-generated response for sensitive-data exposure, unsupported claims and unsafe actions; state what you would test or flag in a code review. The role explicitly mentions quality, compliance, Responsible AI and secure engineering.

Skills to revise

  • Implement ServiceNow application logic

    In a practice scoped application, or in pseudocode if platform access is unavailable, write a JavaScript Business Rule and client script for a simple request and specify a corresponding UI policy. This exercises the platform and JavaScript fundamentals named in the posting.

  • Build a workflow and integration

    Sketch or prototype a Flow Designer flow that calls a REST endpoint through IntegrationHub, handles an error response and records the outcome. App Engine, Flow Designer, IntegrationHub and REST APIs are explicitly listed.

  • Configure an AI-enabled interaction

    If a practice instance is available, configure a small Virtual Agent interaction or Now Assist use case and test how a user reaches relevant AI Search results; otherwise, write the configuration and test steps. These products and AI Search tuning are named in the job.

  • Write prompts and evaluators

    Draft a prompt for summarizing a sample service request, create several expected and adverse test cases, and record where the output fails. The responsibilities explicitly include authoring prompts and evaluators and contributing to testing frameworks.

  • Review an AI-enabled change

    Practice a brief code-review walkthrough of one proposed workflow or prompt change: describe validation, security concerns, test evidence and questions for an architect or product owner. The posting calls for code reviews and collaboration in agile sprints.

04

Your preparation checklist

  • Map the stated requirements

    Prepare a one-page evidence map for ServiceNow, JavaScript, REST APIs and AI work. Note that the posting states both 2–5 years of experience and a minimum of 3 years, plus 15 years of full-time education; prepare a question to clarify the experience wording.

  • Prepare a ServiceNow feature walkthrough

    Choose a real project you can discuss and outline its problem, your contribution, platform components, validation and outcome. Where applicable, connect it to Now Assist, Virtual Agent, AI Search, App Engine Studio, Flow Designer or IntegrationHub; do not imply you used tools you did not use.

  • Rehearse a platform and integration design

    As a practice exercise, sketch a scoped ServiceNow application with a workflow and an external API. Explain data models, Business Rules, client scripts or UI policies where relevant, and how you would handle REST or SOAP, authentication and secure engineering.

  • Build an AI evaluation example

    Prepare a sample prompt, evaluation criteria and test cases for an AI-enabled workflow. Explain how you would check answer quality, failures and Responsible AI concerns; be ready to discuss RAG and vector search at the basic level requested in the posting.

  • Practice an agentic workflow scenario

    Sketch a proposed automation or developer-assistance flow, showing the agent’s permitted actions, checkpoints, error handling and tests. Present it as a design exercise, not as an Accenture project.

  • Prepare collaboration examples and questions

    Select concise examples of working through a sprint, code review or technical disagreement with architects or product owners. Draft questions about the team’s ServiceNow AI use cases, evaluation approach and how quality and compliance are checked in practice.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

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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