New opportunity

AI Solution Engineer

Accenture · Atlanta, United States

About this role

Accenture is partnering with a global AI company to help small and mid-sized businesses turn generative AI opportunities into working solutions. The AI Solution Engineer works with sales and solution engineering teams across customer discovery, technical validation and early onboarding, combining consultative engagement with hands-on solution design.

Responsibilities

Lead technical discovery to understand customers’ business workflows, technology, data, security needs and integration dependencies. Design practical solutions using large language models, APIs, retrieval-augmented generation, tool calling, agentic workflows and enterprise-system connections.
Recommend architectures, configurations and implementation approaches. Advise on API authentication, rate limits, endpoints, performance and data flows; assess when an LLM-based solution is appropriate versus conventional software or automation. Explain trade-offs involving accuracy, grounding, security, privacy, latency, cost and operational support, and build demonstrations for customer use cases.
Discuss data handling, encryption, authentication, access control and compliance with customers. Respond to security questionnaires and support risk assessments with internal security teams.
Collaborate with sales, product and engineering on feasible solution proposals; document architectures, configuration steps and onboarding plans; identify technical blockers and mitigation strategies. Escalate issues, share customer insights, support post-sale technical handoffs and contribute to knowledge bases, FAQs and solution playbooks.

Qualifications

Required: At least 3 years of experience in solutions consulting, sales engineering, solution architecture or technical account management.
Preferred: Hands-on experience building, configuring or evaluating generative AI solutions; familiarity with ChatGPT, Claude, Gemini or Microsoft Copilot; knowledge of LLM APIs, prompt design, structured outputs, tool or function calling, retrieval-augmented generation, embeddings and retrieval techniques. Experience securely integrating AI with enterprise applications and processes, prototyping agentic workflows, and working with evaluation, guardrails, monitoring or observability is preferred. Other preferred knowledge includes Model Context Protocol, RESTful APIs, authentication, integration workflows, error handling, scripting, JSON, SOC 2, ISO 27001 and GDPR. The role calls for explaining architectural choices to technical and non-technical audiences; experience with SMBs or high-velocity sales teams is preferred.

Work arrangement and eligibility: Full-time, in the Atlanta, GA office five days a week. US applicants must have work authorization that does not require visa sponsorship now or in the future. Benefits listed include medical, dental, vision, life and long-term disability coverage, a 401(k), bonus opportunities, paid holidays and paid time off.

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

Skills for this role

Generative AILarge language modelsSolution architectureSolutions consultingSales engineeringTechnical discoveryAPI integrationREST APIsAuthenticationRetrieval-augmented generationEmbeddingsPrompt designStructured outputsTool callingAgentic workflowsModel Context ProtocolAI evaluationAI guardrailsAI monitoringSecurity risk assessmentData privacyEncryptionAccess controlJSONScriptingSOC 2ISO 27001GDPRChatGPTClaude AI assistant platform credential or tool name? Noneed correct skill

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

Lead technical discovery
The posting calls for leading discovery across customer workflows, technology, data, security requirements and integration dependencies, then turning those findings into practical solution designs.
Choose and validate the right approach
Assess whether an LLM is preferable to conventional software or automation; recommend architecture and configuration options, and build demonstrations that test customer use cases and show a credible path to implementation.
Make integration and trade-offs explicit
Advise on API authentication, rate limits, endpoints, performance and data flows. Explain how accuracy, grounding, privacy, latency, cost and operational support affect a proposed design.
Carry proposals through security and handoff
Lead security discussions, respond to questionnaires with internal security support, identify blockers, and document architectures, configuration steps and onboarding plans for cross-functional and post-sale handoffs.

Likely challenges to think through

Balancing speed with credible validation
Preparation hypothesis: Supporting multiple opportunities at pace may make it difficult to demonstrate value without implying a prototype is deployment-ready. Prepare to describe how a focused demo would test a use case, expose limitations and identify remaining implementation work.
Choosing against an LLM when appropriate
Preparation hypothesis: Customer interest in generative AI may conflict with a simpler or more reliable conventional approach. Prepare a decision framework that compares fit, grounding and accuracy needs, latency, cost and ongoing support.
Advancing design while security details are unresolved
Preparation hypothesis: Data access and integration decisions may depend on security review. Prepare to distinguish known controls from open questions, describe mitigations, and explain when to involve internal security teams rather than promise compliance.

Practice your answers

Technical practice

How would you lead technical discovery for an SMB considering generative AI?

What to coverSuggested preparation: Describe how you would map the business workflow, existing systems, data sources, security needs and integration dependencies before proposing an architecture. Explain how you would turn those findings into a use case that can be validated.

When would you recommend an LLM instead of conventional software or automation?

What to coverSuggested preparation: Compare the need for flexible language handling with requirements for predictable, deterministic behavior. Address accuracy, cost, latency and the level of human oversight the workflow can support.

How would you design a retrieval-augmented solution using enterprise data?

What to coverSuggested preparation: Walk through data access, retrieval and embeddings, grounding the response in retrieved material, and respecting access controls. Explain how you would check whether retrieval improves answer quality without exposing information a user should not see.

How would you use prompt design and structured outputs in a customer workflow?

What to coverSuggested preparation: Explain how instructions and an output structure can make responses more usable by downstream systems. Cover validation and what the workflow should do when an output is incomplete or does not match the expected JSON structure.

When would you use tool calling or an agentic workflow?

What to coverSuggested preparation: Explain how a model could invoke a constrained tool to interact with an enterprise system, and when a simpler workflow would be safer or easier to support. As an inferred practice angle, discuss whether an integration standard such as Model Context Protocol would help, without assuming the customer uses it.

How would you troubleshoot an unreliable LLM API integration?

What to coverSuggested preparation: Trace the request and response flow, including authentication, endpoint selection, JSON payloads, rate limits and error handling. Explain how you would distinguish a configuration or integration fault from a model-quality problem.

How would you approach a customer's AI security questionnaire?

What to coverSuggested preparation: Organize the discussion around data handling, encryption, authentication, access control and applicable compliance questions. Explain what you would verify with internal security teams rather than making unsupported claims about platform controls or SOC 2, ISO 27001 or GDPR compliance.

How would you evaluate and monitor an AI solution before broader use?

What to coverSuggested preparation: Define checks tied to the customer's use case, including answer quality, grounding and failure cases. Discuss guardrails, human oversight and monitoring as ways to detect problems after initial validation.

How would you respond if a proposed solution is too slow or costly?

What to coverSuggested preparation: Identify where latency and cost arise across model calls, retrieval and integrations, then compare configuration or architecture changes. Explain how you would test improvements against accuracy, security and ongoing operational support rather than optimizing one measure alone.

What would make a customer demonstration credible as a path to implementation?

What to coverSuggested preparation: Show how the demonstration would test a real use case and expose integration dependencies, technical blockers and limitations. Describe the architecture notes, configuration steps and onboarding plan needed to move from validation toward a feasible handoff.

Behavioural practice

Tell me about a time you reconciled customer value with technical feasibility.

What to coverSuggested preparation: Use a real situation involving stakeholders with different priorities, and explain the actions you took to clarify constraints and shape a proposal. Close with the result and any trade-off you communicated.

Tell me about a time you explained a complex technical choice to a non-technical audience.

What to coverSuggested preparation: Set out the audience's decision and the technical issue, then describe how you adapted your explanation without hiding important limitations. State the result and how you checked understanding.

Tell me about a time you managed several customer opportunities at once.

What to coverSuggested preparation: Describe the situation, competing deadlines and how you prioritized discovery, validation or follow-up actions. Give the result and explain how you kept stakeholders informed.

Tell me about a time you escalated a security or technical risk.

What to coverSuggested preparation: Describe the risk, what you could establish yourself and when you involved the appropriate specialists. Explain your actions to communicate a mitigation or unresolved question, and give the result without overstating certainty.

Tell me about a time you made a technical handoff reusable.

What to coverSuggested preparation: Use a real example of handing work to an implementation or support team, including the situation and documentation you provided. Explain the result and whether you captured lessons in a FAQ, knowledge base or playbook.

Sharpen your knowledge

Topics to prepare

Customer discovery and solution fit
Posting: Lead discovery across workflows, technology, data, security and integration dependencies; judge when an LLM is preferable to conventional software or automation. Revision exercise: For a hypothetical SMB workflow, list discovery questions and compare an LLM approach with a conventional alternative.
LLM solution architecture
Posting: Design with LLMs, APIs, retrieval-augmented generation, tool calling, agentic workflows and enterprise-system connections. Revision exercise: Sketch two architecture options for one use case and explain where retrieval, tools and human oversight belong.
API integration principles
Posting: Advise on authentication, endpoints, rate limits, performance and data flows; RESTful design and error handling are preferred knowledge. Revision exercise: Trace a request through an enterprise integration and explain how authentication, rate limiting and failures affect the design.
Quality and operational trade-offs
Posting: Explain accuracy, grounding, latency, cost and operational-support trade-offs; evaluation, guardrails and monitoring are preferred knowledge. Revision exercise: Define how you would assess answer quality and investigate an inaccurate response in a proposed customer solution.
Security and compliance concepts
Posting: Discuss data handling, encryption, authentication, access control and compliance; knowledge of SOC 2, ISO 27001 and GDPR is preferred. Revision exercise: For a proposed data flow, identify sensitive-data boundaries, access decisions and questions to refer to a security specialist rather than assuming compliance.

Skills to revise

Run a technical-discovery conversation
Posting: The role leads customer discovery alongside sales. Practice suggestion: Role-play a short SMB call, ask about the workflow, data, systems and security constraints, then summarize the use case and unresolved dependencies.
Produce a solution proposal
Posting: Recommend architectures and implementation approaches, document configurations and onboarding plans, and flag blockers. Practice suggestion: Write a one-page design with a data-flow diagram, configuration decisions, blockers, mitigations and first onboarding steps.
Build a use-case demonstration
Posting: Develop demonstrations that test customer use cases; hands-on generative AI experience is preferred. Practice suggestion: Prototype a small retrieval or tool-calling flow using an LLM API, and show both a successful result and a limitation.
Troubleshoot an API interaction
Posting: API integration advice is central; JSON, scripting and light troubleshooting are preferred. Practice suggestion: Inspect a sample JSON request and response, diagnose an authentication error and a rate-limit response, and describe a safe retry or escalation.
Work through a security questionnaire
Posting: Respond to customer security questionnaires and support risk assessments with internal security teams. Practice suggestion: Draft answers about data handling, encryption and access control; mark any platform-specific claims as requiring verification.
Explain and hand off a design
Posting: Explain architectural choices to technical and non-technical stakeholders and support an early post-sale handoff. Practice suggestion: Present the same proposal in a brief customer-facing explanation and a technical handoff noting assumptions, open issues and next steps.

Your preparation checklist

Select evidence from relevant work
The posting requires at least three years in solutions consulting, sales engineering, solution architecture, or technical account management. Prepare a timeline and two specific examples showing technical discovery, customer communication, and collaboration; use only experience you can substantiate.
Draft a customer discovery guide
Prepare questions for a small or mid-sized business about its workflow, systems, data, security needs, integration dependencies, and success criteria. Add a short summary template for turning answers into a proposed solution.
Prepare a generative AI project walkthrough
Choose a real project or clearly label a hypothetical prototype. Show the use case, architecture or demo, your design choices, how you would test output quality, and a credible path from validation to implementation.
Practice an architecture trade-off exercise
Sketch a solution using an LLM API and, where justified, retrieval or tool calling. Compare it with conventional software or automation, and explain accuracy, grounding, privacy, latency, cost, and support trade-offs.
Work through an integration and security scenario
Prepare a sample data-flow diagram covering API authentication, endpoints, rate limits, and error handling. Annotate questions about data handling, encryption, access control, and compliance that would need confirmation with a security team rather than assuming platform capabilities.
Draft a technical handoff
Create a one-page example with architecture, configuration steps, an early onboarding plan, blockers and mitigations, and issues to escalate. The posting calls for documentation and coordination with sales, product, engineering, and Customer Success.
Write focused questions for the employer
Ask how technical validation is scoped across multiple customer opportunities, what makes a demo ready for handoff, and how security questions are routed internally. Confirm the Atlanta five-day in-office arrangement and any practical role expectations you need clarified.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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