New opportunity

Advisory AI Foundry Architect

ServiceNow · Mumbai, India

About this role

ServiceNow seeks an Advisory AI Foundry Architect for its Solution Consulting team in Mumbai. The role is the region’s technical face of Platform and AI, supporting Employee Experience, Customer Workflow and ITX solution sales. It combines hands-on AI development with technical consulting and solution positioning during sales cycles, in partnership with Sales, Product Management and executives.

Responsibilities

Build proofs of concept and accelerators using the platform and AI agents; experiment with LLM improvements, agentic orchestration and workflow optimization. Develop reusable regional components and knowledge assets, and work in sprints with opportunity AI Solution Architects on proofs of concept.
Support product sales as a technical and domain expert. Lead customer discovery workshops and product demonstrations, translate customer challenges into solutions, and answer technical and product questions from customers, channel partners and colleagues.
Work toward quarterly and annual sales goals for an assigned territory. Share reusable assets and best practices, provide customer-driven feedback to Product Management, track competitive differentiation, and support executive briefings, conferences, user groups and trade shows.

Requirements

10+ years of pre-sales solution consulting or sales engineering experience, with proven ability to work directly with customers and translate business problems into technical solutions.
Hands-on experience designing and building agentic AI applications, including multi-agent orchestration, RAG, prompt and context engineering, and evaluation; proficiency with agent frameworks and modern LLM tooling.
Strong understanding of enterprise integration, APIs, data pipelines, and AI security and governance considerations. Proficiency with the ServiceNow platform or technical expertise in cloud software solutions.
Experience applying or critically assessing AI in work processes, decision-making or problem-solving; excellent communication with engineering and executive audiences; collaborative experience with product management, product marketing, partners and professional services; and territory management skills, including pipeline building and execution with a Sales counterpart. Travel as necessary.

The posting labels the Mumbai role “Flexible” but does not specify a particular remote or hybrid arrangement.

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

Skills for this role

AI agentsMulti-agent orchestrationRetrieval-augmented generationPrompt engineeringContext engineeringAI evaluationLarge language modelsAgent frameworksWorkflow optimizationEnterprise integrationAPIsData pipelinesAI securityAI governanceServiceNow platformCloud softwarePre-sales solution consultingSales engineeringProof-of-concept developmentCustomer discoveryProduct demonstrationsTerritory managementCommunication

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

Turn discovery into credible solution positioning
The role leads customer discovery workshops and demonstrations for Employee Experience, Customer Workflow and ITX solution sales. Prepare to explain how a customer problem becomes a product-specific solution and a demonstration relevant to that problem.
Build and evaluate agentic AI proofs of concept
The posting calls for hands-on Platform and AI Agent POCs and accelerators, including experimentation with LLM improvements, orchestration and workflow optimization. Prepare to describe design and evaluation decisions involving multi-agent workflows, RAG, prompt and context engineering.
Design for enterprise constraints
The qualifications emphasize integrations, APIs, data pipelines, and AI security and governance. Prepare to explain how those considerations shape an AI solution rather than treating them as additions after a demo.
Support territory sales execution
The role works with a Sales counterpart toward quarterly and annual territory goals, while answering technical questions and supporting executive briefings. Prepare to show how technical work can advance a sales cycle without overstating what a solution can do.
Scale learning across opportunities
The posting calls for reusable regional components and knowledge assets, sprint-based POC work with opportunity AI Solution Architects, and customer-driven product feedback. Prepare to distinguish what should be reused from what needs to remain customer-specific.

Likely challenges to think through

POC speed versus enterprise rigor
Likely preparation hypothesis: sales-cycle POCs may need to show value quickly while still addressing evaluation, integration and AI governance. Prepare a way to scope a demonstrable workflow, state its limitations, and identify what would need validation before broader use.
Technical depth versus executive clarity
Likely preparation hypothesis: acting as the region’s technical face of Platform and AI requires detailed answers for engineers and clear business positioning for executives. Prepare two concise explanations of the same agentic solution, tailored to each audience without changing the underlying claims.
Reusable assets versus customer fit
Likely preparation hypothesis: regional accelerators can save time, but discovery may reveal needs a standard component does not cover. Prepare to explain how you would decide whether to adapt an asset, build a focused POC, or relay a recurring gap to Product Management.

Practice your answers

Technical practice

Suggested practice: How would you scope a Platform and AI Agents proof of concept for a customer opportunity?

What to coverThe role builds proofs of concept and accelerators during sales cycles. Suggested focus: define the customer problem, a bounded workflow, success criteria, and what the demonstration can and cannot establish.

Suggested practice: When would you use multi-agent orchestration rather than a simpler agent design?

What to coverThe posting calls for hands-on agentic AI development and experimentation with orchestration. Suggested focus: explain task boundaries and coordination, weighing added complexity against the value of specialized agents.

Suggested practice: How would you design retrieval-augmented generation for an enterprise workflow?

What to coverRAG is an explicit qualification for the role. Suggested focus: discuss source selection, retrieval quality, grounding, and how you would investigate an answer that does not reflect the retrieved material.

Suggested practice: How would you improve an LLM application through prompt and context engineering?

What to coverThe role names prompt and context engineering alongside experimentation with LLM improvements. Suggested focus: distinguish changes to instructions from changes to supplied context, and explain how you would test whether either change helps.

Suggested practice: How would you evaluate an agentic AI proof of concept?

What to coverEvaluation is explicitly listed among the required hands-on capabilities. Suggested focus: choose measures tied to the customer workflow, examine failure cases, and explain the trade-off between a compelling demo and dependable performance.

Suggested practice: How would you connect an AI agent to enterprise systems and data?

What to coverThe posting calls for understanding of integration, APIs, and data pipelines. Suggested focus: describe data flow and interface boundaries, then explain how you would diagnose stale data or a failed integration during a proof of concept.

Suggested practice: What security and governance questions would you resolve before positioning an enterprise AI solution?

What to coverAI security and governance are explicit qualifications. Suggested focus: identify the information and actions an agent would access, how access should be constrained, and which risks need discussion with the customer rather than an unsupported assurance.

Suggested practice: How would you decide whether an AI agent improves a work process?

What to coverThe role involves workflow optimisation and critical assessment of AI in work processes and decision-making. Suggested focus: compare the current workflow with the proposed one, including human decision points, measurable benefits, and cases where AI adds complexity.

Suggested practice: How would you troubleshoot a proof of concept whose agent produces inconsistent results?

What to coverThe role combines hands-on proofs of concept with LLM experimentation and evaluation. Suggested focus: isolate retrieval, context, orchestration, and integration as possible causes, then use repeatable tests before changing the design.

Suggested practice: How would you turn discovery findings into a technically credible product demonstration?

What to coverThe role leads discovery workshops, demonstrates solutions, and answers technical questions during sales cycles. Suggested focus: map the customer's stated challenge to demonstrated capabilities, distinguish verified fit from open questions, and explain differentiation without making unsupported product claims.

Behavioural practice

Suggested practice: Tell me about a discovery workshop where the customer's problem was initially unclear.

What to coverThe role requires leading workshops and translating customer challenges into solutions. Suggested response: use situation, action, and result to show how you clarified priorities and what the customer could decide afterward.

Suggested practice: Describe a time you collaborated across roles to deliver a proof of concept under a deadline.

What to coverThe posting describes sprint-based work with opportunity AI Solution Architects and collaboration across teams. Suggested response: give the situation, your actions to coordinate technical scope and ownership, and the result without overstating your individual contribution.

Suggested practice: Give an example of explaining the same technical solution to engineering and executive audiences.

What to coverThe role calls for communication with both audiences and support for executive briefings. Suggested response: set out the situation, how you changed the explanation while preserving its limitations, and the resulting understanding or decision.

Suggested practice: Tell me about customer feedback you brought to product management.

What to coverProviding customer-driven product feedback is a stated responsibility. Suggested response: describe the situation, how you distinguished a recurring need from a one-off request, the action you took, and what resulted.

Suggested practice: Describe how you and a Sales counterpart prioritized technical effort across opportunities.

What to coverThe role includes territory management, pipeline building, and working with Sales toward quarterly and annual goals. Suggested response: explain the situation, your prioritization and coordination actions, and the result, including any trade-off between opportunity urgency and proof-of-concept effort.

Sharpen your knowledge

Topics to prepare

Agentic AI architecture
The posting requires multi-agent orchestration, RAG, prompt and context engineering, and evaluation. Suggested exercise: sketch an agent-assisted workflow and explain when retrieval, multiple agents, and evaluation add value—and when they add complexity.
Enterprise integration and AI governance
The posting calls for understanding of APIs, data pipelines, and AI security and governance. Suggested exercise: diagram how an AI agent would receive data, invoke an enterprise API, and return a result; mark access-control, data-handling, and oversight decisions.
Platform and workflow solution positioning
This role is the regional technical face of Platform and AI and supports Employee Experience, Customer Workflow, and ITX solution sales. Suggested exercise: choose one of those workflow areas and outline how to translate a customer problem into a platform-and-AI solution, distinguishing a demonstrable capability from an assumption.
Pre-sales and territory execution
The posting combines solution consulting with quarterly and annual territory goals, pipeline building, and work with a Sales counterpart. Suggested exercise: map a hypothetical opportunity from discovery through proof of concept and demonstration, noting the technical decision needed at each stage.

Skills to revise

Build an agent proof of concept
The role builds platform-and-AI-agent POCs and accelerators and requires hands-on agent-framework and LLM-tooling proficiency. Suggested exercise: implement a small agentic workflow with a clear business task, then explain its orchestration and what would need to change for reuse.
Test and improve LLM behavior
The posting explicitly names evaluation and experimentation with LLM improvements. Suggested exercise: create a small set of representative queries, compare two prompt or context approaches, and document observed failures and trade-offs.
Conduct discovery and demonstrate a solution
The role leads discovery workshops and product demonstrations aligned to customer needs. Suggested exercise: rehearse a short discovery conversation, turn the answers into a proposed solution, and deliver a focused demonstration narrative for both technical and executive audiences.
Explain an integration design
Enterprise APIs and data pipelines are explicit qualifications. Suggested exercise: present an API-and-data-flow design for the proposed POC, including where security and governance checks belong, and answer likely technical questions.
Create a reusable field asset
The role develops regional components and knowledge assets and shares best practices with other Solution Consultants. Suggested exercise: turn a sample POC into a concise handoff note covering its purpose, assumptions, configuration points, evaluation results, and limits.

Your preparation checklist

Assemble a pre-sales evidence sheet
The posting asks for 10+ years of pre-sales solution consulting or sales engineering, customer-facing problem solving, and territory management. Prepare a one-page record of relevant roles, customer engagements, pipeline contributions, and outcomes you can substantiate.
Prepare an agentic AI project walkthrough
Select a project you can discuss and outline the problem, your contribution, agent orchestration, RAG, prompt and context engineering, framework and LLM tooling choices, evaluation, and measured results. Identify what you would improve.
Sketch an enterprise AI proof of concept
As a preparation exercise, choose an Employee Experience, Customer Workflow, or ITX use case. Draw a proposed workflow showing agents, APIs, data flow, evaluation, and AI security and governance controls; distinguish assumptions from capabilities you have verified.
Rehearse a discovery-to-demo conversation
The role leads discovery workshops and product demonstrations. Draft questions to uncover a customer’s workflow, constraints, and success criteria, then outline how you would position a relevant solution and handle technical questions without overstating product features.
Show how a proof of concept could scale
Prepare a brief example of turning a POC into a reusable component or knowledge asset, including what another regional consultant would need to use it. The posting also describes sprint collaboration with opportunity AI Solution Architects.
Practice an executive and sales-partner briefing
Create a short briefing that translates technical choices into customer value, risks, and differentiation. Be ready to explain how you have worked with Sales on territory execution and with product or partner teams on customer feedback, using only examples you can support.
Bring targeted questions for the employer
Ask how POCs are prioritized with opportunity AI Solution Architects, how reusable assets are adopted across the region, and what distinguishes strong performance against territory goals. Clarify the Mumbai role’s stated “Flexible” work persona and expected travel, since the posting does not specify an arrangement or frequency.

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

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