Hire Enterprise AI Architects
Who Design AI That Fits the Enterprise

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your AI goals, current pilots, data estate, security constraints, vendor commitments, governance pressure, executive timeline, and delivery capacity. The goal is not to send resumes quickly. It is to send Enterprise AI Architects who can turn your portfolio into a fundable, governed, buildable plan.

Yes. You interview the shortlisted architects before committing. We recommend using a real enterprise AI decision in the interview: model gateway versus direct vendor use, RAG over sensitive documents, agent tool access, vendor lock-in, AI system inventory, eval standards, or prioritizing ten proposed use cases into a roadmap. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the architect understands your enterprise AI landscape. You should see a current-state readout, portfolio scoring model, target-state brief, platform tradeoffs, governance implications, cost considerations, risk register, or delivery roadmap tied to real AI initiatives. If progress is unclear, you should know that during the trial, not after a long contract cycle.

An Enterprise AI Architect designs how an organization adopts AI across teams, systems, data sources, vendors, and risk controls. The role defines portfolio roadmap, reference architecture, model access, data permissions, RAG and agent standards, evals, observability, cost controls, governance, and adoption paths. It is broader than one AI app because it decides how many AI apps can scale safely.

Quality is managed through senior screening, role-specific interview criteria, architecture review, governance review, documented decisions, and delivery checkpoints. We look for practical judgment across AI roadmap design, model gateway patterns, RAG architecture, agent governance, data permissions, eval strategy, observability, cost controls, vendor decisions, risk tiers, and executive communication.

Yes. The architect works with your engineering, data, security, legal, procurement, product, operations, and leadership teams. They can review repositories, AI pilots, architecture diagrams, vendor contracts, data platforms, identity systems, governance documents, and roadmaps at the access level you approve. The operating model defines ownership, decision rights, and delivery standards.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, executive reviews, architecture reviews, stakeholder workshops, security reviews, and escalation. For Enterprise AI Architect engagements, the communication rhythm is tied to proof points that matter: roadmap clarity, platform reuse, governance coverage, stakeholder alignment, delivery sequencing, and measurable business cases.

NDA and IP assignment are handled before onboarding. Access is scoped to the architecture, repositories, data catalogs, AI pilots, vendor details, governance artifacts, and platform information required for the scope. Sensitive work follows your rules for least privilege, audit logs, confidential strategy, data classification, legal review, and approval workflows.

Use the risk-free trial to evaluate whether the architect can understand your enterprise context, communicate with executives and technical teams, make vendor-neutral recommendations, and turn ambiguity into clear architecture decisions. If the fit is wrong, we replace the architect within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one architect and expand only if execution requires it. Common expansion paths include Forward-Deployed Engineers for first deployments, AI Governance Engineers for risk and audit operations, AI Platform Engineers for shared services, Data Engineers for source readiness, AI Security Engineers for threat modeling, and Product Engineers for user-facing workflows.

Typical options include an AI Strategy and Architecture sprint, a fractional or full-time Senior Enterprise AI Architect, or an Architect plus FDE plus Governance Lead pod for strategy and first execution. We confirm the model after discovery so you can compare a focused architecture sprint, a dedicated architect, or a small pod against the actual risk: fragmented pilots, governance gaps, vendor lock-in, data-access risk, or unclear AI ROI.

We can support both models. If you already have strong product, engineering, and transformation leadership, the architect can plug into your process. If you need more structure, Devlyn can add delivery oversight, stakeholder coordination, workshop planning, reporting, and senior technical review around roadmap, platform, governance, and first-delivery milestones.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing senior AI architecture talent. That matters because the wrong architect can push vendor bias, strategy decks, or isolated pilots while the organization still lacks platform standards and governance. You get a shorter path to qualified architects and a trial focused on concrete architecture output.

Devlyn is a better fit when AI architecture affects executive commitments, sensitive data, security review, enterprise integrations, vendor spend, customer workflows, compliance, or long-term platform direction. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes like roadmap clarity, reference architecture, governance, and reusable platform standards.

The strongest fit is work where AI must scale across teams safely. Common examples include enterprise AI roadmap creation, architecture review board setup, model gateway strategy, RAG over internal knowledge, agent governance, AI system inventory, AI platform standards, eval and observability strategy, data-access policy, vendor selection, cost governance, and AI adoption planning.