New opportunity

AI Native Software Engineer

Accenture · Bengaluru, India

About this role

Build and deliver production-ready, AI-driven ServiceNow solutions across multiple client engagements, using AI throughout the engineering lifecycle while maintaining quality, security, compliance and measurable business outcomes. This is an associate manager role based in Bengaluru.

Responsibilities

Own end-to-end solution delivery and establish architecture standards, governance, security and observability for AI features.
Lead client discovery workshops, develop AI roadmaps and align solutions with business priorities. Introduce generative AI and agentic patterns into ServiceNow workflows.
Mentor and manage cross-functional delivery teams, promote engineering excellence and a product mindset, and work with leadership to scale AI adoption, manage risks and sustain delivery.

Requirements

At least 7.5 years of experience and 15 years of full-time education. The posting also labels the role with an experience band of 5–10 years.
Advanced ServiceNow architecture expertise, including App Engine, Flow Designer, IntegrationHub and Scoped Apps; proficiency in Virtual Agent, AI Search optimization and Now Assist configuration.
Understanding of LLM integration, prompt engineering, evaluation frameworks, agentic design patterns, orchestration, and RAG and vector search concepts. Familiarity with Azure OpenAI, AWS/GCP AI services and enterprise AI-model observability.
Ability to implement Responsible AI, governance and compliance practices, and experience with REST/SOAP integrations, MID Server and secure authentication using OAuth and SAML.
Proven agile delivery-team leadership, stakeholder management, client communication, strategic thinking, negotiation, 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 AssistLarge Language ModelsPrompt EngineeringAI EvaluationAgentic AIAI OrchestrationRAGVector SearchAzure OpenAIAWS AI servicesGCP AI servicesAI ObservabilityResponsible AIAI GovernanceRESTSOAPMID ServerOAuthSAMLAgile DeliveryStakeholder ManagementClient CommunicationTeam 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

End-to-End AI-Driven ServiceNow Delivery
The role requires owning the full lifecycle of AI-integrated ServiceNow solutions. You must demonstrate the ability to bridge complex ServiceNow architecture (App Engine, Flow Designer, IntegrationHub) with modern AI capabilities like Now Assist, RAG, and agentic workflows to deliver measurable business outcomes.
AI Governance and Responsible Implementation
As an Associate Manager, you are responsible for establishing architecture standards, security, and observability for AI features. You must prioritize the implementation of Responsible AI frameworks and compliance standards to ensure that AI-driven workflows remain secure and reliable in enterprise client environments.
Strategic Client Advisory and Roadmap Development
You must lead discovery workshops and translate business needs into actionable AI roadmaps. This requires balancing technical feasibility with client business priorities, effectively managing stakeholder expectations, and driving the adoption of generative AI patterns within existing ServiceNow ecosystems.
Cross-Functional Team Leadership and Mentorship
The role demands fostering a product mindset and engineering excellence within delivery teams. You must be prepared to coach teams on both technical AI integration and agile delivery practices while managing the risks associated with scaling AI solutions across multiple client engagements.

Likely challenges to think through

Balancing Legacy Integration with Agentic AI
Integrating modern agentic patterns and LLMs into established ServiceNow environments often involves complex REST/SOAP integrations and MID Server configurations. Prepare by articulating how you handle the technical friction between legacy system constraints and the non-deterministic nature of generative AI models.
Managing AI Model Observability and Performance
Ensuring production-ready AI requires robust evaluation frameworks and observability. You may face difficulty in maintaining consistent performance and accuracy for AI features in a multi-client context; prepare by discussing your approach to monitoring model drift, latency, and output quality in enterprise settings.
Navigating Change Management in AI Adoption
Introducing AI-native practices often meets resistance due to security or operational concerns. You must be prepared to demonstrate how you use negotiation and strategic communication to align leadership and end-users on the value of AI while mitigating risks related to compliance and data governance.

Practice your answers

Technical practice

ServiceNow Architecture for AI Integration

What to coverExplain how you would architect a solution that integrates LLMs into ServiceNow using App Engine and IntegrationHub. Focus on how you ensure security, scalability, and maintainability when connecting external AI services to scoped applications.

Implementing Agentic Patterns in ServiceNow

What to coverDescribe how you would design an agentic workflow within ServiceNow to automate complex tasks. Discuss the orchestration logic required to manage agent state, tool selection, and the hand-off between AI agents and human users.

RAG and Vector Search Optimization

What to coverHow do you approach building a Retrieval-Augmented Generation (RAG) pipeline for enterprise data within ServiceNow? Address the trade-offs between vector search accuracy and latency, and how you would handle data privacy and access control.

AI Model Observability and Governance

What to coverWhat metrics and monitoring strategies would you implement to ensure the reliability of AI features in production? Discuss how you track model performance, drift, and compliance with Responsible AI standards in an enterprise environment.

Now Assist and Virtual Agent Configuration

What to coverCompare the use cases for Now Assist versus traditional Virtual Agent flows. Explain how you would optimize AI Search to improve the relevance of responses provided by these tools.

Secure Integration Patterns

What to coverWhen integrating ServiceNow with external AI services like Azure OpenAI or AWS Bedrock, how do you manage authentication and data transit? Explain your approach to using OAuth, SAML, and MID Servers to maintain a secure perimeter.

Prompt Engineering and Evaluation Frameworks

What to coverDescribe your process for developing and testing prompts for enterprise AI applications. Suggest how to build an evaluation framework to measure the quality, safety, and business impact of LLM outputs.

Managing AI Compliance and Responsible AI

What to coverHow do you ensure that AI-driven solutions comply with corporate governance and data privacy regulations? Provide examples of how you would implement guardrails to prevent bias and hallucinations in automated workflows.

Behavioural practice

Leading Client Discovery Workshops

What to coverDescribe a time you led a discovery workshop to define a technical roadmap for a client. Focus on how you translated complex business priorities into actionable AI-driven technical requirements.

Managing Cross-Functional Delivery Teams

What to coverProvide an example of how you have mentored a team to adopt a product mindset while delivering technical projects. Explain how you balanced technical excellence with the need to meet aggressive delivery timelines.

Navigating Stakeholder Conflict

What to coverTell me about a situation where you had to negotiate with stakeholders who had conflicting views on an AI implementation strategy. How did you align them with the project's goals and manage the change process?

Driving Innovation in Complex Environments

What to coverShare an experience where you introduced a new technology or pattern into an existing, stable system. How did you manage the risks associated with the transition and ensure the team remained productive?

Sharpen your knowledge

Topics to prepare

Agentic Design Patterns and Orchestration
The role requires integrating agentic patterns into ServiceNow workflows. Exercise: Map out a multi-step business process (e.g., IT incident resolution) and design an orchestration flow where an LLM agent autonomously triggers sub-tasks, manages state, and handles tool execution. This is relevant for demonstrating your ability to move beyond simple chatbots to autonomous AI-driven workflows.
Responsible AI and Governance Frameworks
You must implement governance and compliance for AI features. Exercise: Draft a checklist for an AI-driven ServiceNow application that covers data privacy, bias mitigation, and auditability. Define how you would monitor model performance and compliance in a production environment. This is critical for the role's requirement to own security and compliance standards.
Enterprise AI Observability
The role demands maintaining observability for AI features. Exercise: Research and document a strategy for monitoring LLM-based applications, focusing on latency, token usage, hallucination rates, and feedback loops. This is essential for the requirement to sustain delivery and manage risks in client engagements.

Skills to revise

ServiceNow Advanced Architecture
You need to demonstrate expertise in App Engine, Flow Designer, and Scoped Apps. Exercise: Design a complex, scoped ServiceNow application architecture that utilizes IntegrationHub to connect with an external LLM API. This directly addresses the core technical requirement for building production-ready AI solutions.
RAG and Vector Search Implementation
The role requires knowledge of RAG and vector search. Exercise: Create a technical design document explaining how you would index ServiceNow knowledge base articles into a vector database and configure a retrieval pipeline for a Now Assist implementation. This is vital for the role's focus on enhancing AI search and content generation.
Prompt Engineering and Evaluation
You must be proficient in prompt engineering and evaluation frameworks. Exercise: Develop a set of test cases and evaluation metrics (e.g., relevance, accuracy, safety) for a specific prompt used in a ServiceNow Virtual Agent scenario. This is necessary for the role's requirement to validate AI solutions.

Your preparation checklist

Map ServiceNow Architecture to AI Integration
Prepare a technical walkthrough demonstrating how you have integrated LLMs or Now Assist with ServiceNow components like Flow Designer and IntegrationHub. Focus on how you handled secure authentication (OAuth/SAML) and MID Server configurations while maintaining enterprise-grade observability.
Develop an Agentic Workflow Case Study
Draft a project summary detailing your experience with agentic design patterns and RAG. Explain how you implemented vector search and prompt engineering to solve a specific business problem, including how you measured the AI's performance and ensured compliance with Responsible AI standards.
Prepare for Strategic Leadership Discussions
Formulate examples of how you have led client discovery workshops and defined AI roadmaps. Be ready to discuss your approach to managing cross-functional teams, scaling AI adoption within a large organization, and navigating the risks associated with production-ready AI deployments.
Formulate Targeted Questions for the Interviewer
Prepare questions regarding Accenture's current governance frameworks for AI, how they balance rapid innovation with strict compliance requirements in client engagements, and how the team measures the success of AI-driven ServiceNow solutions.

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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