Sr Solution Sales - AI Platform

What matters most

• Turn enterprise concerns into architectures

  The posting assigns this role technical discovery and solution design for multi-stakeholder customers across Europe, the Middle East and South Asia. The focus is translating sovereignty, risk, governance, cost and control concerns into clear, evidence-backed solutions.

• Own technical credibility through the sale

  The role works with sales leadership, researchers and engineers from first conversation to deal closure, including executive presentations to CXOs and AI, risk and engineering leaders. The posting explicitly distinguishes this from running delivery projects.

• Make written commitments rigorous and feasible

  The role owns the technical narrative in proposals, RFPs and regulatory questionnaires, checking accuracy and feasibility with research and engineering teams. This matters particularly for the high-risk domains named in the posting.

• Demonstrate production-grade AI capabilities

  The posting calls for supporting advanced demos and proofs of concept involving governed LLM gateways, explainability, alignment and fine-tuning pipelines, and runtime observability. It also calls for discussion of evaluation and deployment trade-offs beyond surface-level tooling.

• Bring customer evidence back to the business

  The role feeds structured customer insights into product, platform and go-to-market strategy, rather than treating each engagement as an isolated sales conversation.

Likely challenges to think through

• Reconciling competing decision criteria

  Likely preparation hypothesis: AI, risk, engineering and executive stakeholders may weigh control, cost and evidence differently. Prepare a discovery framework that identifies each concern and shows how an architecture addresses trade-offs without claiming every objective can be maximized.

• Keeping persuasive claims defensible

  Likely preparation hypothesis: a strong demo or proposal may invite broader claims than research and engineering can substantiate, especially in a regulatory questionnaire. Prepare to distinguish demonstrated evidence, design assumptions and capabilities that still need validation.

• Explaining deep ML trade-offs at two levels

  Likely preparation hypothesis: researchers may expect technical precision while executives need a concise decision rationale. Practice explaining one evaluation or deployment trade-off in both forms, retaining its limitations and governance implications.

Topics to prepare

• AI sovereignty, governance and auditability

  The posting emphasizes sovereign, governed and defensible AI systems. Revise by sketching how an enterprise could retain control over model choices and produce evidence of system behavior; distinguish what an architecture could demonstrate from claims that would need validation.

• Governed LLM gateways and runtime observability

  The role supports discussions of governed LLM gateways and runtime observability. Revise by drawing a request-to-response flow and identifying where controls, monitoring and audit evidence would fit. Treat the design as a preparation exercise, not a stated platform specification.

• Explainability, alignment and evaluation

  The posting calls for discussions of explainability, alignment and evaluation with researchers and senior stakeholders. Revise by comparing what each addresses, its limitations and the evidence you would request before making a customer-facing claim.

• Fine-tuning and deployment trade-offs

  The role covers fine-tuning pipelines and deployment trade-offs, while customer conversations include cost and control. Revise by comparing a hypothetical fine-tuned approach with an alternative model choice, stating assumptions, evaluation needs and operational trade-offs rather than prescribing one approach.

• Evidence in high-risk domains

  The posting names banking, financial services and insurance as settings where regulation and evidence matter. Revise by taking a hypothetical high-risk use case and listing the questions a risk leader might ask, the evidence needed to answer them and what remains unknown; do not assume a particular regulation applies.

Skills to revise

• Lead multi-stakeholder technical discovery

  The role explicitly leads discovery with CXOs and AI, risk and engineering leaders. Practice a 20-minute discovery outline that elicits business goals, sovereignty concerns, risk, cost and control, then separates confirmed needs from assumptions.

• Shape an evidence-backed solution

  The posting requires translating customer concerns into clear architectures and evidence-backed solutions. Practice turning your discovery notes into a one-page architecture with alternatives, feasibility questions for researchers and engineers, and claims requiring proof.

• Write accurate RFP and regulatory responses

  The role owns the technical narrative in proposals, RFPs and regulatory questionnaires. Draft two sample answers about auditability and model optionality; label verified facts, proposed validation and unanswered questions instead of inventing platform capabilities.

• Present to executive and technical audiences

  Executive-level presentations and communication without excessive technical detail are explicit expectations. Practice a three-minute explanation of a proposed governed AI architecture for a CXO, then a deeper version covering evaluation and deployment trade-offs for an engineering leader.

• Scope a demo or proof of concept

  The role supports advanced demos and proofs of concept. As a preparation exercise, outline a hypothetical demonstration of an LLM gateway or explainability capability, including the customer question, observable evidence, success criteria and limitations; do not imply a particular implementation exists.

• Synthesize customer insights

  The posting asks this role to feed structured customer insights into product, platform and go-to-market strategy. Practice converting mock discovery notes into a short brief separating recurring needs, objections, evidence gaps and suggested follow-up with internal teams.

• Prepare an AI product walkthrough

  Choose an AI product you built or shipped. Document your decisions, evaluation approach, deployment trade-offs and measurable outcomes; distinguish your contribution from the team’s.

• Rehearse enterprise discovery

  Draft questions for AI, risk, engineering and executive stakeholders about sovereignty, governance, cost and control. Practise turning their answers into a concise problem statement.

• Sketch a governed AI architecture

  As a preparation exercise, diagram a mission-critical use case with model options, an LLM gateway, audit evidence and runtime observability. Note assumptions and trade-offs rather than presenting it as the company’s design.

• Outline a proof of concept

  Prepare a proposed demo or proof-of-concept plan for explainability, evaluation or alignment. Specify what it would demonstrate, what evidence it would produce and what remains unproven.

• Draft a sample technical response

  Write a short response to a hypothetical RFP or regulatory question. Separate supported claims from items requiring confirmation with researchers or engineers, reflecting the role’s emphasis on accuracy and feasibility.

• Practise an executive briefing

  Prepare a brief presentation that explains why governance, auditability and model optionality matter, with a technical appendix for deeper questions.

• Bring role-clarifying questions

  Ask how pre-sales work is divided among sales, research and engineering; how customer insights reach product teams; and how the posted Sr Solution Sales title relates to the Senior AI Solutions Architect description.