Hire AI Platform Engineers
Who Build the Tools AI Teams Use Every Day

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your AI roadmap, internal users, model providers, platform stack, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send AI Platform Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test internal developer experience, model gateways, evaluation services, guardrail layers, RAG platform design, observability, cost controls, and platform boundaries. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the engineer understands your system and can move real work forward. For this role, you should see a platform capability or service improvement with API contract, sample usage, adoption notes, governance considerations, telemetry, and rollout risks. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce a reusable AI platform layer for model access, evaluations, guardrails, observability, internal APIs, and developer experience. The outcome should be measurable through platform adoption, time to first AI feature, shared-service reuse, gateway reliability, eval coverage, prompt regression pass rate, trace coverage, policy coverage, cost visibility, and internal developer satisfaction.

Quality is managed through senior screening, role-specific interview criteria, code or architecture review, documented decisions, and delivery checkpoints. For AI Platform Engineer work, we look for evidence across model gateway design, eval systems, RAG services, agent infrastructure, OpenTelemetry-style observability, tenant boundaries, security policies, internal SDKs, documentation, cost controls, and production handover.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, observability stack, internal developer channels, and communication rhythm. For AI Platform Engineer work, we define the operating model explicitly: model gateway rules, service ownership, platform APIs, eval standards, cost ownership, and access policies are designed for teams to reuse.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For AI Platform Engineer engagements, the communication rhythm is tied to the proof points that matter: platform adoption, time to first AI feature, shared-service reuse, policy coverage, reliability, cost visibility, and internal developer satisfaction.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the AI Platform Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can handle internal developer experience, model gateways, evaluation services, guardrail layers, observability, RAG platform design, platform boundaries, and communication with internal users. 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.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include AI infrastructure engineering for runtime scaling, security engineering for policy controls, data engineering for RAG connectors, MLOps for model operations, QA for eval harnesses, and product engineering for internal developer tools.

Typical options include Model Gateway + Eval Library ($32,000 fixed scope) 6 weeks, senior platform engineer, Senior AI Platform Engineer ($6,000/mo) Full-time, 5–10+ years, Platform + MLOps + Security ($24,000/mo) 3-person pod, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual AI Platform Engineer requirement.

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, and senior technical review around model gateways, eval hubs, RAG services, agent tooling, observability, and developer experience.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For AI Platform Engineer hiring, that matters because the real risk is every team building AI differently, duplicating infrastructure, ignoring governance, and making pilots hard to scale. You get a shorter path to qualified candidates and a trial structure focused on technical outcomes rather than resume volume.

Devlyn is a better fit when the AI Platform Engineer work affects production systems, internal developer velocity, customer workflows, security, cost, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around outcomes like a reusable AI platform layer for model access, evaluations, guardrails, observability, internal APIs, and developer experience.

AI Platform Engineers are a strong fit when multiple teams need shared AI infrastructure and governance. Common use cases include enterprise model gateways, reusable RAG layers, prompt evaluation hubs, agent developer platforms, model usage telemetry, LLM observability, internal SDKs, prompt registries, cost allocation, tenant-aware policy controls, self-serve AI onboarding, eval dashboards, and platform APIs for product teams. If the need is narrower, we can help you decide whether one specialist, a full-time dedicated engineer, or a small delivery pod is the right model.