New opportunity

Enterprise AI Architect

Distyl AI · San Francisco; New York, United States

About this role

Distyl AI seeks a senior Enterprise AI Architect in Engineering to be a long-term technical partner to strategic Fortune 500 customers. This individual contributor works with Go-to-Market, Product and Forward-Deployed Engineering to turn business ambitions into feasible AI solutions, remaining accountable for the technical relationship beyond contract signature.

Responsibilities

Build trusted relationships with CIO/CTO-level leaders, enterprise architects and business executives; identify where AI can materially improve operations and shape a deliverable technical vision.
Lead customer solution architecture, feasibility assessments, prototypes and demos. Evaluate data access, existing systems, integrations, security, deployment models and organizational constraints before committing to scope.
Own the technical content of statements of work (SOWs), including dependencies, assumptions and risks. Partner with forward-deployed engineers on qualified late-stage implementations.
Remain involved after close for architecture reviews, executive steering, major tradeoffs, risk escalations and expansion, while the Technical Delivery Lead owns day-to-day execution. Turn recurring customer needs and deployment lessons into reusable assets and product feedback.

Required qualifications

8+ years of relevant experience in engineering, enterprise architecture, forward-deployed engineering, solutions architecture or similar roles, including substantial customer-facing responsibility.
Experience designing, building and shipping enterprise-grade agentic AI/ML systems; ability to assess reliability, integration, scale, security and implementation tradeoffs.
Proven ownership of complex enterprise architecture and scope, technical SOW writing, executive advisory relationships and coordination across customer, GTM, delivery and product teams.
Working knowledge of APIs, integrations, cloud and enterprise architecture, data flows, security and modern AI systems, plus coding fluency for prototyping and technical collaboration. Deep expertise in at least one area such as customer support, voice, MarTech, cybersecurity, IAM or document processing.

Preferred

Experience owning customer outcomes and technical decisions in forward-deployed engineering, engineering or technical consulting; partner-like work involving long C-suite-led sales cycles; or early-stage startup experience.

The role requires 25% travel and a hybrid schedule at the San Francisco or New York office for 3+ days weekly, including Tuesday–Thursday. Base salary is $200,000–$275,000 depending on experience, location and level, with equity, health coverage, flexible time off, retirement and financial-planning benefits, wellness benefits and in-office meals.

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

Skills for this role

Enterprise AI architectureSolution architectureAgentic AIMachine learningForward-deployed engineeringPrototypingAPIsSystems integrationCloud architectureEnterprise architectureData flowsSecurityTechnical feasibility assessmentStatement of work (SOW) developmentExecutive stakeholder managementCodingCustomer supportVoiceMarTechCybersecurityIdentity and access management (IAM)Document processing

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

Own the executive technical relationship
The architect is the primary technical counterpart for strategic accounts, advising CIO/CTO-level leaders, enterprise architects and business executives from early problem shaping through expansion. Preparation suggestion: be ready to explain how you translate an operational goal into an ambitious but deliverable technical vision.
Qualify opportunities before committing to scope
The role calls for a technical feasibility judgment based on data access, existing systems, security, integrations and organizational constraints. Preparation suggestion: organize an example of how you tested assumptions and decided what was buildable.
Make architecture and SOWs survive delivery
The architect leads joint solution architecture, uses prototypes or demos where needed, and owns technical SOW content covering integrations, deployment, dependencies, assumptions and risks. Preparation suggestion: trace one architecture decision from initial evidence through scope and delivery implications.
Apply production agentic AI judgment
The posting requires experience designing, building and shipping enterprise-grade agentic AI/ML systems, with credible judgment on reliability, scale, security and operating constraints. Preparation suggestion: explain concrete tradeoffs rather than treating a successful prototype as proof of production readiness.
Maintain continuity and reuse deployment lessons
After close, the Technical Delivery Lead owns day-to-day execution while this architect remains involved in reviews, steering, major decisions and escalations. The role also turns recurring customer needs into reusable assets and product feedback. Preparation suggestion: distinguish strategic oversight from delivery ownership and show how lessons can inform future work.

Likely challenges to think through

Ambition versus feasible commitment
Potential challenge, not a confirmed employer problem: an executive vision for AI may run ahead of available data, integrations or organizational readiness. Prepare to explain how you would validate the highest-risk assumptions, communicate limits to senior stakeholders and narrow scope before a commitment.
Prototype promise versus production behavior
Potential challenge: a persuasive demo may not establish reliability, security or scale in an enterprise workflow. Prepare to describe what a prototype would test, what evidence would still be missing and how those gaps would affect architecture or SOW assumptions.
Accountability without day-to-day control
Potential challenge: remaining accountable for the technical relationship while the Technical Delivery Lead runs execution could blur decision boundaries. Prepare to describe how you would preserve architecture continuity, involve forward-deployed engineers at the right time and escalate consequential tradeoffs without taking over delivery.

Practice your answers

Technical practice

How would you decide whether an enterprise operation is a good fit for AI?

What to coverThe role calls for qualifying opportunities before committing to scope. Suggested answer focus: define the operational value, then test data availability, system access, security, integration, and organizational constraints before recommending a feasible path or declining the use case.

How would you architect an enterprise-grade agentic AI system for production?

What to coverThe posting requires experience shipping agentic AI/ML systems. Suggested answer focus: explain the components and data flows, where the system can act independently, how its actions are controlled, and the trade-offs among reliability, integration, security, and operating constraints.

How would you map data flows and APIs across a customer's existing systems?

What to coverJoint architecture and enterprise integration are explicit responsibilities. Suggested answer focus: identify data owners, access paths, interface constraints, and failure points, then explain how those findings change the solution design.

How would security requirements affect an AI solution's deployment model?

What to coverThe role evaluates security, data access, and deployment models during feasibility and SOW work. Suggested answer focus: compare feasible deployment options against customer constraints, and make unresolved dependencies and risks explicit rather than assuming access will be granted.

An agentic system works in a demo but becomes unreliable at production scale. How would you investigate?

What to coverThe posting emphasizes production reliability, scale, and operating constraints. Suggested answer focus: distinguish model behavior from integration and data-flow failures, define evidence to collect, and explain how you would prioritize fixes against the customer's operational needs.

What would you prototype before committing to an enterprise AI scope?

What to coverThe role uses prototypes and demos to validate key assumptions and calls for coding fluency. Suggested answer focus: choose the highest-risk assumption, build only enough to test it, and state what result would change the architecture or feasibility decision.

How would you write the technical portion of an AI implementation SOW?

What to coverThe architect owns technical SOW content, including integrations, data access, deployment model, dependencies, assumptions, and risks. Suggested answer focus: make scope and decision points clear enough that customer teams, GTM, and delivery share a buildable understanding.

How would your domain expertise change an AI architecture decision?

What to coverThe posting seeks deep expertise in at least one area such as customer support, voice, MarTech, cybersecurity, IAM, or document processing. Suggested answer focus: use one domain you know to explain its workflow and constraints, and show how those specifics affect feasibility and design.

How would you assess a major architecture trade-off after delivery has begun?

What to coverThe architect stays involved in reviews and difficult decisions while the Technical Delivery Lead owns day-to-day execution. Suggested answer focus: compare options against the agreed scope and production constraints, document the risk, and explain how you would support a decision without taking over delivery.

How would you turn deployment lessons into reusable technical assets?

What to coverThe role calls for translating recurring customer needs and deployment patterns into assets and product feedback. Suggested answer focus: distinguish a repeatable pattern from a customer-specific exception, then describe what evidence would make feedback useful to Product and Engineering.

Behavioural practice

Tell me about a time you built trust with both executives and technical leaders.

What to coverThe role serves as a long-term technical counterpart to both groups. Suggested preparation: use a real situation, your actions to reconcile business ambitions with engineering constraints, and the resulting decision or relationship outcome.

Describe a time you pushed back on an ambitious but infeasible technical scope.

What to coverThe posting makes this architect accountable for feasibility and credible commitments. Suggested preparation: cover the situation, the evidence and alternatives you presented, your actions with stakeholders, and the resulting scope decision.

Tell me about a time you aligned sales, engineering, and delivery around a complex solution.

What to coverThe role works across GTM, Forward-Deployed Engineering, Product, and customer teams. Suggested preparation: explain the situation, how you brought in implementation expertise and clarified ownership, and what happened at handoff.

Describe a high-stakes technical escalation you handled after a customer commitment.

What to coverThe architect remains involved after close for risk escalations and executive steering, without owning daily delivery. Suggested preparation: give the situation, your actions to clarify trade-offs and support the delivery lead, and the result.

Tell me about a time an ambiguous customer need revealed a reusable pattern.

What to coverThe role turns recurring needs and deployment lessons into technical assets and product feedback. Suggested preparation: describe the situation, how you tested whether the pattern extended beyond one customer, what you shared with relevant teams, and the outcome.

Sharpen your knowledge

Topics to prepare

Production-grade agentic AI/ML tradeoffs
The posting requires experience shipping enterprise-grade agentic AI/ML systems and reasoning about reliability, scale, security, integration and operating constraints. Revision exercise: compare two possible designs for an enterprise workflow and explain where each could fail in production and what evidence would change your choice.
Enterprise systems and data boundaries
The role calls for working knowledge of APIs, integrations, cloud and enterprise architecture, data flows and security. Revision exercise: trace a hypothetical workflow from source data through an AI component to an existing business system; identify access, integration and deployment decisions.
AI opportunity qualification
The architect must decide whether an opportunity suits AI and assess data, systems, security, integration and organizational constraints before committing to scope. Revision exercise: take an ambiguous operations request and write a brief feasibility decision, including unresolved assumptions and a reason to defer or narrow scope.
Technical ownership across the account lifecycle
The posting spans early problem shaping, architecture and SOW through post-close reviews, steering and escalation; the Technical Delivery Lead owns day-to-day execution. Revision exercise: map decisions and handoffs across those stages, noting when to involve GTM, forward-deployed engineers, Product and delivery.
Depth in one enterprise domain
Deep expertise in at least one area such as customer support, voice, MarTech, cybersecurity, IAM or document processing is required. Revision exercise: choose a domain you know and explain a material workflow change AI could support, the domain-specific constraints and the limits of the proposal.

Skills to revise

Draw and defend a customer solution architecture
The role leads joint architecture and evaluates data access, integrations, deployment models and security. Practice sketching an architecture for a hypothetical enterprise workflow, then explain its dependencies and tradeoffs to both an enterprise architect and a business executive.
Prototype a critical assumption
The posting calls for coding fluency and prototypes or demos that validate key assumptions. As a preparation exercise, build a small, bounded proof of concept using an API and sample data; state exactly what its result establishes and what remains unproven about production use.
Draft technical SOW content
The architect owns technical SOW quality, including integrations, data access, deployment model, dependencies, assumptions and risks. Practice writing those sections for a hypothetical qualified opportunity, with explicit boundaries between validated facts and work still requiring discovery.
Run an executive technical conversation
The role requires advising CIO/CTO-level and business leaders, shaping ambiguous problems and handling difficult decisions. Rehearse a short discovery conversation: identify the operational objective, present a feasible technical point of view and explain a consequential tradeoff without promising an unvalidated outcome.
Turn deployment lessons into reusable guidance
The posting asks the architect to convert recurring customer needs, security constraints and deployment lessons into reusable assets and product feedback. Practice turning a hypothetical architecture-review issue into a one-page pattern: when it applies, its limitations and the specific feedback it would send to Product.

Your preparation checklist

Prepare a production AI system walkthrough
If you have a relevant example, outline your role in designing, building and shipping an enterprise agentic AI/ML system. Bring an anonymized architecture diagram and evidence of how you addressed reliability, scale, security and operating constraints.
Frame a domain-specific opportunity
Choose an enterprise area you know well, such as customer support, voice, IAM or document processing. Prepare a one-page explanation of the operational problem, where AI could create value and what would make the use case unsuitable.
Practice an enterprise feasibility assessment
For a hypothetical customer opportunity, produce a go/no-go assessment covering data access, existing systems, APIs, integrations, deployment options, security and organizational constraints. Label unknowns and the evidence needed before committing to scope.
Sketch a solution and validation prototype
Draw a proposed architecture and identify one key assumption a small coded prototype or demo could test. Explain the result that would change the design; treat this as interview preparation, not a claim about Distyl’s platform.
Draft a technical SOW excerpt
Write a sample scope section with integrations, data dependencies, deployment assumptions, risks and boundaries. Show how you would align the customer, Go-to-Market and delivery teams on what is buildable.
Rehearse an executive advisory conversation
Prepare a brief account of how you shaped an ambiguous business request into a technical direction, or a hypothetical approach if you lack a suitable example. Practice explaining a difficult tradeoff to CIO/CTO-level and technical stakeholders.
Map the relationship beyond signature
Outline how you would stay involved in architecture reviews, executive steering and risk escalations while the Technical Delivery Lead owns daily execution. Include an example of turning a deployment lesson into a reusable asset or product feedback.
Bring targeted questions for the employer
Ask how technical feasibility decisions are made, when forward-deployed engineers join a pursuit, and how architects and delivery leads divide post-close decisions. If relevant, clarify the stated 25% travel and Tuesday–Thursday office schedule.

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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