Hire Full-Stack AI Engineers
Who Ship the Interface, Backend, and AI Workflow Together

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after discovery. For this role, discovery is not limited to years of experience and framework preferences. We ask what AI product slice you need to ship, which users it serves, whether the feature needs streaming responses, retrieval, tool calling, background jobs, approval steps, analytics, or model cost controls, and what codebase the engineer will join. That context lets us shortlist engineers who can contribute to the actual product path instead of sending generic full-stack resumes.

Yes. You interview shortlisted engineers before committing. We recommend using the interview to test cross-layer judgement: how they would build a streamed AI assistant, where they would store conversation state, how they would validate structured model output, how they would handle failed retrieval, how they would protect tool calls that can change data, and how they would measure latency, cost, and user completion. That makes the interview practical for a CTO because the conversation is about production behavior, not just React, Node.js, Python, or prompt-writing keywords.

The first week should produce evidence that the engineer can operate inside your product, not just talk about AI. Depending on scope, you should see a working branch, staged workflow, architecture note, or hardening plan tied to a real feature. Examples include a streamed response path with loading and error states, a retrieval-backed answer flow with source display, a tool-backed action that requires user approval, or a document workflow that extracts data and routes exceptions. You should also see release risks: missing permissions, slow responses, weak eval coverage, unsafe output rendering, cost concerns, or unresolved data quality issues.

A strong hire should deliver production-shaped AI product capability. That means users can complete a task, the interface explains what the system is doing, the backend safely orchestrates model calls and tools, data access follows your permissions model, outputs are validated where needed, failures have fallback states, and engineering can inspect what happened. The outcome should be measurable through activation, task completion, accepted recommendations, latency, tool-call success, API reliability, frontend errors, cost per successful task, and the maintainability of the feature path.

Quality starts with screening for engineers who have shipped real product flows across the stack. We look for frontend judgement, API design, database and queue experience, model provider integration, retrieval basics, structured outputs, tool-call safety, testing discipline, and communication clarity. During delivery, quality is reinforced through code review, architecture notes, eval examples, telemetry, staged releases, and explicit decision records. For AI-heavy features, we also watch for risks documented by modern LLM security guidance, including prompt injection, insecure output handling, sensitive information exposure, excessive agency, and unbounded model consumption.

Yes. The engineer can join your repositories, issue tracker, design tools, standups, code review process, CI, staging environment, observability stack, and release cadence. We define the operating model early so the role does not float between product, frontend, backend, data, and platform. UI states, API contracts, prompts, tool definitions, retrieval rules, test fixtures, and deployment notes are kept close to the work so your internal team can review and maintain the system.

Yes. Devlyn plans overlap windows for interviews, standups, product reviews, architecture decisions, and urgent release discussions. For this role, overlap matters because the engineer will often need product, design, backend, security, and data context in the same week. We set the communication rhythm around the proof points that matter: shipped branch, demo, open risks, review feedback, latency, error states, eval coverage, and the next decision needed from your team.

NDA and IP assignment are handled before onboarding. Access is scoped to the repositories, tools, model providers, datasets, environments, and logs required for the engagement. For AI product work, we also clarify whether prompts, uploaded files, conversation logs, embeddings, analytics events, and model outputs may contain sensitive data. The engineer works inside your access control, review, audit, and approval process so the AI feature does not create a side channel around your normal security rules.

Use the risk-free trial to evaluate real evidence: how quickly the engineer understands your product, whether they can debug across frontend and backend, how they reason about model behavior, whether their code is reviewable, and whether they communicate risks without hiding uncertainty. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

Yes. Many teams start with one senior Full-Stack AI Engineer to prove the first product slice, then add adjacent capacity when the surface area grows. Common expansion paths include a product designer for AI interaction design, a retrieval engineer for knowledge-heavy systems, a platform engineer for model gateway and observability, a data engineer for pipelines and quality, a security engineer for sensitive workflows, or a QA and automation engineer for release confidence.

Typical options include a fixed-scope AI feature sprint, a dedicated senior Full-Stack AI Engineer, or a small product cell with engineering, design, and AI workflow support. We confirm the right model after discovery because a dashboard copilot, AI-native SaaS MVP, document workflow, and enterprise internal assistant all carry different risks. The goal is to match pricing to the proof you need: first release, embedded capacity, or a sustained product build.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, senior technical review, and demo discipline. For this role, project management is most useful when it keeps product behavior, AI workflow quality, security review, and release readiness connected instead of letting each layer become a separate conversation.

Sourcing a strong Full-Stack AI Engineer is difficult because the role sits between product engineering, backend systems, AI integration, data access, and release operations. A resume can look strong while hiding weak judgement around model failure, insecure output rendering, background job reliability, or cost control. Devlyn reduces that screening burden and gives you a trial structure focused on technical evidence: can the engineer ship the AI product slice inside your environment, communicate tradeoffs, and leave maintainable patterns behind?

Devlyn is a better fit when the work affects production systems, customer workflows, sensitive data, model cost, security review, or long-term maintainability. A freelance marketplace can work for isolated tasks, but full-stack AI product work usually needs continuity, code review, replacement support, IP protection, and delivery governance. You are not only buying implementation time; you are reducing the risk that the AI feature becomes a fragile demo that no one on your team wants to own.

This role is strongest when the feature needs product and AI workflow ownership at the same time. Good fits include AI SaaS MVPs, embedded copilots, customer-support assistants, internal knowledge tools, workflow automation screens, document intake and review queues, content generation tools, sales or finance assistants, and admin dashboards that need model-backed recommendations. If the work is mostly model training, GPU serving, data platform engineering, or pure prompt experimentation, we may recommend a more specialized role instead.