New opportunity

AI Native Software Engineer

Accenture · Bengaluru, India

About this role

Accenture seeks an AI Native Software Engineer at the Associate Manager level in Bengaluru. The role builds and ships production-ready software using AI throughout the engineering lifecycle, with a focus on AI-driven ServiceNow solutions. The posting lists full-time employment, a 5–10-year experience band, a minimum requirement of 7.5 years of experience, and 15 years of full-time education.

Responsibilities

Own end-to-end delivery across multiple client engagements and deliver measurable business outcomes. Set architecture, governance, security, compliance and observability standards for AI features.
Lead discovery workshops, define AI roadmaps and align solutions with client priorities. Introduce generative AI and agentic patterns into ServiceNow workflows.
Mentor and manage cross-functional teams, support AI adoption with leadership, manage risks and promote sustainable delivery and engineering practices.

Required capabilities:

Advanced ServiceNow architecture expertise, including App Engine, Flow Designer, IntegrationHub and Scoped Apps; proficiency with Virtual Agent, AI Search optimization and Now Assist configuration.
Understanding of LLM integration, prompt engineering, evaluation frameworks, agentic design patterns, orchestration, RAG and vector search. Familiarity with Azure OpenAI, AWS or GCP AI services, and enterprise AI observability.
Ability to apply Responsible AI, governance and compliance practices, and work with REST/SOAP integrations, MID Server, OAuth and SAML.
Experience leading agile delivery teams, managing stakeholders and communicating with clients; strategic thinking, a product mindset, coaching and change-management skills.

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

Skills for this role

ServiceNowApp EngineFlow DesignerIntegrationHubScoped AppsVirtual AgentAI SearchNow AssistLLM integrationPrompt engineeringAI evaluationAgentic design patternsAI orchestrationRAGVector searchAzure OpenAIAWS AI servicesGCP AI servicesAI observabilityResponsible AIAI governanceRESTSOAPMID ServerOAuthSAMLAgile deliveryStakeholder managementTeam leadership

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

Deliver outcomes across client engagements
The role owns end-to-end delivery of AI-driven ServiceNow solutions across multiple engagements, with measurable business outcomes. Prepare examples that connect delivery decisions to an outcome and show how progress was measured.
Set ServiceNow solution architecture
The posting calls for advanced ServiceNow architecture across App Engine, Flow Designer, IntegrationHub and Scoped Apps, plus proficiency in Virtual Agent, AI Search and Now Assist. Prepare to explain architectural choices and workflow trade-offs, not just feature configuration.
Make AI features production-ready
The role introduces GenAI and agentic patterns into ServiceNow workflows and calls for LLM integration, evaluation frameworks, orchestration and RAG/vector-search knowledge. Prepare to explain how an AI feature would be validated, operated and improved after release.
Own governance and operational standards
The role defines and enforces architecture, security, compliance and observability standards for AI features, including Responsible AI practices. Prepare examples of how standards shaped an implementation and how risks were monitored.
Lead clients and delivery teams
The posting emphasizes discovery workshops, AI roadmaps, stakeholder alignment, agile team leadership, coaching and change management. Prepare to show how client priorities became a delivery plan and how cross-functional teams stayed aligned.

Likely challenges to think through

Balancing innovation with controls
Preparation hypothesis: Introducing agentic workflows while enforcing security, compliance and observability may require trading autonomy or speed for control. Prepare to explain where you would set boundaries, evaluate behavior and retain oversight.
Keeping multiple engagements outcome-focused
Preparation hypothesis: Owning delivery across clients may make it difficult to give each solution enough architectural attention while maintaining measurable progress. Prepare an example of prioritizing work, delegating decisions and tracking outcomes.
Turning discovery into sustainable adoption
Preparation hypothesis: A client’s interest in AI may not translate directly into a feasible roadmap or lasting workflow change. Prepare to explain how you would reconcile business priorities with technical constraints, delivery risks and team readiness.

Practice your answers

Technical practice

How would you architect an AI-driven ServiceNow solution using App Engine and Scoped Apps?

What to coverThe posting calls for advanced ServiceNow architecture expertise and end-to-end delivery across client engagements. Preparation suggestion: explain application boundaries, maintainability, and how you would validate that the design supports a measurable business outcome.

When would you use Flow Designer and IntegrationHub in a ServiceNow workflow?

What to coverBoth are named in the posting’s architecture requirements. Preparation suggestion: compare workflow automation with external-system integration, including how you would handle failures and avoid unnecessary complexity.

How would you introduce Now Assist into a ServiceNow workflow alongside Virtual Agent?

What to coverThe role calls for Now Assist configuration, Virtual Agent proficiency, and GenAI in ServiceNow workflows. Preparation suggestion: discuss the user interaction, where AI adds value, and how you would validate output quality before production use.

How would you evaluate and improve AI Search in ServiceNow?

What to coverAI Search optimization is an explicit skill in the posting. Preparation suggestion: describe how you would assess result relevance and user outcomes, then investigate poor results and weigh improvements against operational effort.

How would you integrate an LLM and evaluate its responses?

What to coverThe posting names LLM integration, prompt engineering, evaluation frameworks, and familiarity with cloud AI services. Preparation suggestion: cover prompt design, representative test cases, failure analysis, and the trade-off between response quality, latency, and cost.

When would you use RAG and vector search for a ServiceNow AI feature?

What to coverRAG and vector search concepts are listed in the qualifications. Preparation suggestion: explain retrieval and grounding, how you would assess source relevance, and when a simpler approach would be preferable.

How would you design an agentic workflow without losing control of its actions?

What to coverThe role includes introducing agentic patterns into ServiceNow workflows and knowledge of orchestration. Preparation suggestion: discuss bounded actions, decision points, human review where warranted, and evaluation of task success and failure.

How would you secure an integration between ServiceNow and an external system?

What to coverThe posting names REST/SOAP, MID Server, OAuth, and SAML. Preparation suggestion: explain how you would choose an integration pattern, handle authentication and access, and troubleshoot connectivity or authorization failures.

What governance and observability standards would you set for an AI feature in production?

What to coverThe role owns architecture, security, compliance, observability, and Responsible AI practices. Preparation suggestion: address controls, monitoring, incident investigation, and how evaluation results would inform changes after release.

How would you validate that an AI-driven ServiceNow release is production-ready and delivering value?

What to coverThe role calls for production-ready software, lifecycle validation, sustainable delivery, and measurable business outcomes. Preparation suggestion: connect quality and compliance checks with operational readiness and an outcome measure agreed with the client.

Behavioural practice

Tell me about a time you turned a client discovery discussion into a delivery roadmap.

What to coverThe posting calls for discovery workshops, AI roadmaps, and alignment with business priorities. Preparation suggestion: use a real situation, your actions in resolving competing priorities, and the resulting decision or outcome.

Tell me about a time you led delivery across competing engagements or priorities.

What to coverThe role owns delivery across multiple client engagements and leads agile teams. Preparation suggestion: give a real situation, how you coordinated work and managed trade-offs, and the result.

Tell me about a time you coached a cross-functional team through a difficult delivery challenge.

What to coverMentoring, team management, and engineering excellence are stated responsibilities. Preparation suggestion: describe the specific situation, your coaching actions, and what changed for the team or delivery.

Tell me about a time you influenced stakeholders to adopt a new technical approach.

What to coverThe posting emphasizes client communication, influencing decisions, change management, and scaling AI adoption. Preparation suggestion: explain the real situation, how you addressed concerns without overstating benefits, and the outcome.

Tell me about a time you managed risk in a complex client environment.

What to coverRisk management, negotiation, adaptability, and sustainable delivery are part of the role. Preparation suggestion: set out the situation, the actions you took to surface and manage the risk, and the result.

Sharpen your knowledge

Topics to prepare

ServiceNow architecture and delivery boundaries
The posting calls for advanced ServiceNow architecture across App Engine, Flow Designer, IntegrationHub and Scoped Apps. For revision, sketch a hypothetical solution and explain which responsibilities belong in each component and how you would govern changes across client engagements.
ServiceNow AI capabilities
The role names Virtual Agent, AI Search optimization and Now Assist configuration. For revision, compare their purposes in a hypothetical workflow and explain how you would decide whether each adds measurable value.
LLM and agentic design
The posting names LLM integration, prompt engineering, evaluation frameworks, agentic patterns, orchestration and RAG/vector search. For revision, diagram a hypothetical ServiceNow workflow using these concepts, then identify where evaluation and human oversight would fit. The diagram is a preparation exercise, not a stated project.
Responsible AI and operational controls
The role requires governance, compliance, security and observability for AI features. For revision, draft a review checklist covering data handling, failure behavior, evaluation evidence and operational monitoring, and explain how each control supports production delivery.
Client strategy and delivery leadership
The posting emphasizes discovery workshops, AI roadmaps, measurable outcomes, agile delivery, stakeholder management and coaching. For revision, outline how you would turn hypothetical workshop findings into a prioritized roadmap, outcome measures and team delivery decisions.

Skills to revise

Design a ServiceNow solution
Practice drawing an end-to-end design using App Engine, Flow Designer, IntegrationHub and Scoped Apps, then explain architectural trade-offs. These components are explicitly named in the posting; the design scenario is only an interview-preparation suggestion.
Configure ServiceNow AI features
Rehearse the configuration steps you know for a hypothetical Virtual Agent interaction, AI Search improvement and Now Assist use case, including how you would check results. The posting explicitly seeks proficiency in these features.
Plan secure integrations
Work through a sample integration plan covering REST or SOAP, MID Server where relevant, and OAuth or SAML authentication; identify data flows and failure points. These integration and authentication patterns are listed in the role.
Evaluate an AI-enabled workflow
Create a small hypothetical set of prompts, expected results and failure cases for an LLM-assisted ServiceNow workflow; explain how you would assess quality and monitor it after release. This rehearses the posting’s prompt-engineering, evaluation and AI-observability requirements.
Facilitate a discovery discussion
Role-play a client workshop: elicit priorities, define a measurable outcome, surface risks and summarize an initial AI roadmap. The exercise reflects the posting’s client-facing strategy and delivery-leadership responsibilities.

Your preparation checklist

Build a requirements evidence sheet
The posting calls for advanced ServiceNow expertise, AI integration and delivery leadership. Prepare a one-page map of those requirements to specific examples you can substantiate; leave gaps explicit rather than inventing experience.
Rehearse a ServiceNow delivery walkthrough
Prepare an end-to-end example showing architecture decisions, your contribution, deployment and measurable outcomes. Where applicable, address App Engine, Flow Designer, IntegrationHub, Scoped Apps, Virtual Agent, AI Search and Now Assist.
Sketch an AI-enabled workflow
As a practice exercise, diagram a ServiceNow workflow using an LLM or agentic pattern. Explain when you would use orchestration, RAG or vector search, how prompts would be tested, and where a person would retain control.
Diagram integrations and safeguards
Prepare a design sketch covering relevant REST or SOAP integrations, MID Server, OAuth or SAML, data boundaries and failure handling. Annotate how you would address the posting’s security, compliance and Responsible AI expectations.
Draft a production validation plan
Outline evaluation criteria, baseline measures, AI observability, monitoring and a response to poor results. Tie the proposed measures to business outcomes rather than assuming an AI feature is valuable because it ships.
Prepare client and team leadership examples
Select examples you can substantiate of discovery, roadmap prioritization, agile delivery, stakeholder decisions, coaching or change management. State the trade-offs you handled and the outcomes you measured.
Write questions for the employer
Ask which ServiceNow workflows are priorities, how client outcomes are measured, who owns AI governance and production monitoring, and how delivery responsibilities span multiple engagements.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…