New opportunity

Forward Deployed Engineer (FDE) – Marketing & Sales AI Transformation

Lenovo · Morrisville, United States of America

About this role

Lenovo seeks a Forward Deployed Engineer embedded in Marketing and Sales to advance its Solutions and Services Group’s AI-led transformation. The role spans Market-to-Opportunity and Quote-to-Cash processes, with a focus on measurable improvements in revenue growth, sales productivity, pricing and cycle time. This is a full-time, hybrid position in Morrisville, North Carolina, United States of America.

Responsibilities

Work with Marketing and Sales leaders to assess workflows in lead generation, pipeline management, pricing, quoting and order fulfillment; identify high-value opportunities and turn business problems into scoped AI use cases.
Own sprint-based delivery from discovery through prototyping, testing, production deployment and adoption. Build solutions using generative and agentic AI, predictive analytics, RAG pipelines, agent orchestration and automation, including tools in the Microsoft AI ecosystem.
Integrate solutions with enterprise CRM, CPQ and ERP systems. Improve pipeline conversion, quote-to-cash execution, pricing and sales effectiveness.
Drive change management and user adoption, gather feedback and refine solutions. Align work with the digital transformation roadmap, KPIs and ROI targets; contribute to dashboards, scorecards, weekly reviews and steering-committee updates.
Coordinate with business stakeholders, IT, data platforms and AI teams, communicating progress, risks and trade-offs to technical and non-technical audiences.

Qualifications

At least 5 years of experience in software engineering, applied AI or solutions engineering, plus demonstrated delivery of AI or automation solutions in business settings.
Knowledge of LLMs, prompt engineering, RAG and agent-based systems; experience integrating enterprise platforms through APIs; exposure to Marketing, Sales or Revenue Operations; and an ability to deliver measurable outcomes and drive adoption.
The posting also lists experience with Copilot Studio, Azure OpenAI and Power Platform; commercial transformation, pricing or sales enablement; consulting, solutions architecture or cross-functional delivery; and transformation governance, KPI and ROI tracking. Strong communication across business and technical stakeholders is expected.

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

Skills for this role

Generative AIAgentic AIPredictive analyticsLarge language modelsPrompt engineeringRAGAgent orchestrationCopilot StudioAzure OpenAIMicrosoft 365Power PlatformCRMCPQERPAPI integrationSoftware engineeringSolutions engineeringAI solution deploymentAutomationPricing optimizationSales operationsChange managementStakeholder communicationKPI trackingROI tracking

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

Find commercially valuable AI use cases
The role assesses Marketing and Sales workflows from lead generation through order fulfillment, then scopes opportunities against commercial KPIs. Prepare to explain how you would turn a workflow bottleneck into an executable use case with a measurable outcome.
Deliver AI solutions through production
The FDE owns sprint-based delivery from discovery and prototyping through testing, deployment and adoption. The posting names generative and agentic AI, predictive analytics, RAG, and the Microsoft AI ecosystem, with integration into CRM, CPQ and ERP systems.
Improve Market-to-Opportunity and Quote-to-Cash results
The stated outcomes include pipeline conversion, revenue velocity, pricing optimization and quote-to-cash cycle time. Prepare to connect technical design choices to one of these business measures rather than describing a model in isolation.
Secure adoption and demonstrate value
The role embeds solutions in business workflows, gathers feedback, and contributes to KPI and ROI tracking, dashboards, weekly reviews and steering-committee updates. It also requires clear communication of progress, risks and trade-offs across business and technical teams.

Likely challenges to think through

Choosing scope without losing business impact
Preparation hypothesis: a high-value commercial problem may span several teams, while sprint delivery requires a bounded first release. Prepare a way to prioritize use cases by expected impact and feasibility, define a testable scope, and state what would wait.
Moving quickly while integrating enterprise systems
Preparation hypothesis: rapid AI prototyping may be easier than delivering a reliable solution connected to CRM, CPQ or ERP workflows. Prepare to describe how you would test an integration, handle failures and decide when a prototype is ready for production.
Reconciling deployment with adoption and ROI
Preparation hypothesis: a deployed tool may not change sales or pricing outcomes unless people use it in their existing workflow. Prepare to explain how you would gather user feedback, track usage alongside a commercial KPI, and refine the solution if results lag.

Practice your answers

Technical practice

Suggested practice question: How would you turn a Marketing or Sales workflow problem into a scoped AI use case?

What to coverThe role calls for assessing lead generation, pipeline management, pricing, quoting, and order fulfillment against commercial priorities. Suggested answer focus: identify the workflow bottleneck, users, available data, success metric, and smallest testable scope before choosing an AI approach.

Suggested practice question: When would you use RAG for a Sales-facing LLM solution?

What to coverThe posting names LLMs, prompt engineering, and RAG pipelines. Suggested answer focus: explain when retrieval is preferable to prompting alone, how you would select and refresh source material, and how you would test whether answers are grounded.

Suggested practice question: How would you decide whether a workflow needs an agent rather than simpler automation?

What to coverThe role includes agentic AI, agent orchestration, and automation. Suggested answer focus: compare flexibility with complexity and risk, including tool access, approval points, failure handling, and when a deterministic workflow would suffice.

Suggested practice question: How would you evaluate predictive analytics for pipeline conversion?

What to coverThe posting connects predictive analytics with pipeline conversion and sales effectiveness. Suggested answer focus: define the prediction target and baseline, check data quality and leakage, and assess whether the result improves a decision rather than only a model metric.

Suggested practice question: How would you approach an AI use case for pricing optimization?

What to coverPricing optimization is an explicit outcome for this role. Suggested answer focus: distinguish a recommendation from an automated price change, discuss commercial constraints and human review, and propose measures of impact alongside potential unintended effects.

Suggested practice question: What would you consider when integrating an AI solution with CRM, CPQ, and ERP systems?

What to coverThe posting requires production-grade integration with these enterprise systems and cites API experience. Suggested answer focus: address data ownership, API boundaries, access controls, synchronization, error handling, and how the solution fits the existing workflow.

Suggested practice question: How would you choose components from the named Microsoft AI ecosystem for a prototype?

What to coverThe role names Copilot Studio, Azure OpenAI, M365, and Power Platform. Suggested answer focus: explain a use-case-led selection, the integration each component must support, and the trade-offs you would validate before production deployment.

Suggested practice question: How would you take an AI prototype through testing and production deployment?

What to coverThe FDE owns sprint-based delivery from discovery through deployment and adoption. Suggested answer focus: describe representative testing, quality and failure criteria, operational monitoring, and a feedback loop for iteration.

Suggested practice question: How would you reduce quote-to-cash cycle time without losing control of deal execution?

What to coverThe posting identifies quote-to-cash cycle time and deal execution as impact areas. Suggested answer focus: map the handoffs and delays first, then discuss which steps could be assisted or automated and how you would check for errors and exceptions.

Suggested practice question: How would you demonstrate the value of a deployed Sales AI solution?

What to coverThe role aligns delivery with KPIs and ROI targets and contributes to dashboards and scorecards. Suggested answer focus: establish a baseline, define adoption and business-outcome measures, and distinguish observed improvement from impact attributable to the solution.

Behavioural practice

Suggested practice question: Tell me about a time you clarified an ambiguous business problem before building a solution.

What to coverDiscovery and executable use-case definition are central to this role. Use a real situation, your actions to align stakeholders on scope and a measurable outcome, and the result; avoid presenting a hypothetical project as personal experience.

Suggested practice question: Describe a time you resolved a disagreement between business and technical stakeholders.

What to coverThe FDE bridges business teams, IT, data platforms, and AI teams. Give a real situation, the actions you took to surface constraints and explain trade-offs, and the result for the decision or delivery.

Suggested practice question: Tell me about a time user feedback changed a solution after an initial release.

What to coverThe posting emphasizes adoption, change management, and continuous enhancement. Describe the situation, how you gathered and acted on feedback, and the result for workflow use or effectiveness.

Suggested practice question: Describe a time you had to make a delivery trade-off within a short iteration.

What to coverThe role uses sprint-based prototyping, testing, and iteration. Use a real situation, explain what you prioritized or deferred and why, and state the result, including any risk you managed.

Suggested practice question: Tell me about a time you communicated progress and risks to senior stakeholders.

What to coverThe posting calls for clear updates and contributions to weekly reviews and steering-committee inputs. Describe the situation, how you presented evidence and trade-offs to the audience, and the resulting decision or action.

Sharpen your knowledge

Topics to prepare

Market-to-Opportunity and Quote-to-Cash
The posting covers lead generation, pipeline management, pricing, quoting and order fulfillment. For revision, draw both process flows and identify where conversion, pricing or cycle time could be measured.
Choosing an AI approach
The role names generative and agentic AI, predictive analytics, RAG and automation. For revision, compare which approach could address a lead, pricing or quoting problem, including when a simpler automation would suffice.
Enterprise integration concepts
Production solutions must integrate with CRM, CPQ and ERP systems, and the qualifications mention APIs. For revision, sketch the data flow for a quoting use case and identify system boundaries, data dependencies and failure points.
Commercial value and governance
The role aligns delivery with KPIs, ROI targets, dashboards, scorecards and governance reviews. For revision, work through how to define a baseline, calculate a change in quote-to-cash cycle time and distinguish that measure from ROI.
Adoption and cross-functional delivery
The FDE works with business, IT, data-platform and AI teams and is responsible for adoption. For revision, map who would need to approve, use and support a proposed solution, and how feedback would inform the next sprint.

Skills to revise

Scope a business use case
Practice turning a hypothetical pipeline-management complaint into a one-page use case with the workflow, users, success metric, dependencies and sprint-sized first release. This mirrors the posting’s discovery and scoping responsibilities; the hypothetical is a practice exercise, not a stated Lenovo project.
Prototype and evaluate an AI workflow
Using sample data, practice outlining a RAG or agent-based prototype with Azure OpenAI or Copilot Studio, then specify test cases for answer quality, unsupported claims and failure handling. The posting names these approaches and Microsoft tools and expects rapid iteration.
Plan an API integration
Draft a sample field mapping and request/response flow between CRM, CPQ and ERP for a quote update; include error and retry cases. This exercises the enterprise-platform integration explicitly required by the posting.
Move from sprint prototype to deployment
Prepare a sample sprint plan showing prototype, testing, production deployment and feedback checkpoints, with a short list of release risks. The posting assigns the FDE ownership from discovery through deployment and adoption.
Report measurable outcomes
Build a sample scorecard with a baseline, target and observed result for pipeline conversion and quote-to-cash cycle time, plus an ROI calculation with stated assumptions. This practices the posting’s KPI, ROI and governance reporting work.
Communicate adoption and trade-offs
Rehearse a brief stakeholder update explaining a solution’s progress, risks, trade-offs and user-feedback plan in business language, with technical detail ready for IT and data teams. The posting explicitly calls for cross-functional communication and change enablement.

Your preparation checklist

Map a commercial workflow
The role covers Market-to-Opportunity and Quote-to-Cash. Prepare a one-page map of a lead-generation, pricing, quoting or order-fulfillment workflow, with discovery questions, bottlenecks and one scoped AI use case. Treat the map as an exercise, not a description of Lenovo’s current process.
Prepare an AI delivery walkthrough
Choose a real business AI or automation project, if available. Summarize the problem, your responsibilities, sprint decisions, prototype, testing, deployment and results. Distinguish measured outcomes from estimates; the posting emphasizes delivery through adoption.
Sketch an enterprise AI solution
As a design exercise, diagram a RAG or agent-based solution for the scoped use case. Show where an LLM, prompts, orchestration, human review and CRM, CPQ or ERP APIs would fit, and explain how you would test it. Be ready to discuss the Microsoft AI tools named in the posting without claiming experience you do not have.
Assemble evidence for the stated qualifications
Make a brief evidence sheet for the posting’s 5+ years of relevant engineering experience, business AI delivery, enterprise integrations, Marketing or Sales exposure, and stakeholder communication. Note which examples demonstrate measurable outcomes and which qualifications you would need to discuss more cautiously.
Plan adoption and value measurement
Draft a simple rollout plan covering users, feedback and iteration. Pair it with proposed baseline and post-launch measures, such as pipeline conversion or quote-to-cash cycle time; these are preparation suggestions, not Lenovo targets.
Practice a cross-functional update
Prepare a short mock weekly update for business leaders, IT, data-platform and AI teams: progress, KPI movement, risks, trade-offs and the next decision needed. The posting calls for weekly reviews and steering-committee inputs.
Bring questions about scope and governance
Ask which M2O or Q2C workflows are priorities, what CRM, CPQ and ERP integrations are in scope, how production readiness is decided, and how adoption, KPIs and ROI are tracked against the SSG transformation roadmap.

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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