AI Technical Architect

What matters most

• Executive AI advisory

  The role advises C-suite and VP stakeholders and shapes AI strategy and architecture roadmaps. Prepare to explain how business objectives become practical technical decisions.

• Production-grade GenAI architecture

  The posting calls for scalable AI solutions and hands-on production experience with agentic systems, RAG, evaluation, and Bedrock, Azure OpenAI, and Vertex AI. Prepare architecture examples that explain design choices and production trade-offs.

• Standards across engagements

  The architect owns governance and consistent patterns across accounts while mentoring architects and engineering managers. Prepare to describe how standards are established, communicated, and adapted.

• Technical pre-sales ownership

  Leading proposals, statements of work, and RFP responses is an essential function. Prepare to show how an architectural approach can be scoped and explained clearly to a client.

• Frontier tooling judgment

  The role evaluates emerging tooling for the practice, and the qualifications call for research credentials specifically related to harnesses. Prepare to explain how you would assess a new approach against established alternatives.

Likely challenges to think through

• Innovation versus production reliability

  Likely preparation challenge, not a stated employer problem: frontier agentic tools may be promising without being proven for a particular use case. Prepare a comparison that uses evaluation results and operational considerations to justify adoption or restraint.

• Consistency versus client fit

  Likely preparation challenge: shared architecture standards must work across engagements without ignoring distinct client needs. Prepare an example of a reusable pattern, the conditions for an exception, and how that exception would be governed.

• Technical ownership without daily delivery

  Likely preparation challenge: the role sets direction and prototypes hands-on but does not own day-to-day embedded delivery. Prepare to explain how you would transfer decisions to delivery teams and remain accountable for architectural coherence.

Topics to prepare

• Agent orchestration and interoperability

  Draw how LangGraph, Temporal, and MCP could each fit into an agentic system; explain their distinct roles and trade-offs. The posting explicitly names all three and agent evolution approaches.

• Retrieval architecture

  Compare when a RAG design would use vector databases, graph databases, or both, including how retrieved information reaches an LLM. RAG and both database types are explicit qualifications.

• LLM adaptation

  Explain when fine-tuning might be preferable to retrieval, then compare LoRA and hypernetworks at a conceptual level. The posting explicitly calls for production experience with these fine-tuning approaches.

• Evaluation and harnesses

  Outline how an evaluation framework would test answer quality and agent behavior before and after a change. Evaluation approaches are required, and the research-credentials qualification specifically mentions harnesses.

• Multi-cloud AI architecture

  Prepare a comparison of architectural considerations for Bedrock, Azure OpenAI, and Vertex AI without assuming the services are interchangeable. The posting calls for depth across all three platforms.

• Architecture leadership and client advisory

  Rehearse explaining how a business objective becomes an AI roadmap, architecture standards, and governance across accounts. These are stated responsibilities for a role that sets direction rather than handling day-to-day embedded delivery.

Skills to revise

• Prototype an agent workflow

  Sketch or implement a small LangGraph workflow, then show where Temporal or MCP would—and would not—add value. Hands-on prototyping and production experience with the named agentic stack are explicit in the posting.

• Build a retrieval proof of concept

  Create a small RAG flow and document how a vector-database or graph-database choice affects retrieval. This practices the production-oriented RAG and database experience the role requests.

• Draft an evaluation harness

  Define a small set of test cases, expected behaviors, and a repeatable way to compare agent changes. This is a preparation exercise for the posting’s evaluation, agent evolution, and harness references, not a stated deliverable.

• Make a cloud-platform decision

  Write a one-page decision record comparing Bedrock, Azure OpenAI, and Vertex AI for a hypothetical client need; state assumptions and unresolved questions. This practices the required multi-cloud depth and client advisory work.

• Prepare a technical pre-sales response

  Draft a short response to a hypothetical AI RFP with a proposed architecture, scope, assumptions, and delivery boundaries. The role explicitly leads proposals, SOWs, and RFP responses.

• Define reusable architecture standards

  Draft a brief review checklist for an agentic engagement and explain how architects could apply it consistently across accounts. The posting assigns ownership of architecture standards, governance, and mentoring.

• Prepare a production-system walkthrough

  Choose a relevant AI and data system you can discuss. Prepare a diagram and concise account of your architecture decisions, path from concept to production, outcomes, and your technical leadership contribution. The posting emphasizes production-grade systems and architecture leadership.

• Practice an agentic architecture design

  As a preparation exercise, sketch a scalable GenAI or agentic solution and explain where LangGraph, Temporal, RAG, vector or graph databases, MCP, evaluation, and fine-tuning would—or would not—fit. Include trade-offs and a small prototype you could describe.

• Build a cloud-platform comparison

  Prepare a one-page decision matrix for Bedrock, Azure OpenAI, and Vertex AI, covering architecture trade-offs and the information you would need before recommending one. The role calls for depth across all three.

• Draft an executive roadmap and governance example

  Practice turning a business objective into a phased AI architecture roadmap for a C-suite or VP audience. Add an example of standards you would keep consistent across engagements and how you would guide other architects and engineering managers.

• Outline a technical pre-sales response

  Create a sample proposal or RFP-response outline with a solution approach, assumptions, scope boundaries, risks, and open questions. Use it to explain how you would connect client needs to practical technical outcomes; the role includes proposals and SOWs.

• Gather evidence for research and leadership requirements

  Make a short evidence sheet covering architecture tenure, production GenAI work, and any applicable advanced degree, publications, patents, or open-source contributions—especially work on harnesses. Include a specific mentoring example if available; do not imply experience you cannot substantiate.

• Prepare questions about the role’s boundaries

  Bring questions about how hands-on prototyping differs from day-to-day embedded delivery, how architecture standards are applied across accounts, and what ownership of proposals and tooling evaluation looks like in practice.