Shared AI infrastructure
Hire AI Platform Engineers who make AI delivery reusable, observable, and safe.
Get a dedicated AI Platform Engineer to build model gateways, eval hubs, RAG services, agent infrastructure, observability, cost controls, and developer tooling. Shortlist in 48 hours. Two-week paid trial in your codebase. Starts at $2,500/mo.
Direct answer
What does AI Platform Engineer own?
An AI Platform Engineer is the right hire when multiple teams are shipping AI and need shared infrastructure instead of scattered wrappers. This role owns model gateways, provider routing, eval hubs, observability, cost allocation, internal SDKs, deployment controls, reusable RAG/agent services, and governance patterns.
Hiring problem
Hire this role when AI delivery is fragmenting across teams.
Every team builds its own prompt wrappers, model access patterns, eval spreadsheets, vector stores, traces, and cost tracking. AI delivery becomes fragmented before it scales.
- Model gateway
- Provider routing
- Rate limits and fallbacks
- Prompt/eval platform
- RAG platform services
- Agent infrastructure
- Observability/tracing
- Cost allocation
- Internal SDKs
- Deployment/release controls
- Governance policies
- First AI experiment with no proven use case
- One product feature only
- Pure app-layer implementation
- Security review only
First 14-day proof
The trial should create evidence, not just activity.
Gateway or platform slice
A thin vertical slice that centralizes model access for one team or workflow. It proves the platform pattern on something real before any big migration.
API contract
Defines how product teams call models, evals, traces, and shared services. A clear contract is what lets teams build without each reinventing access.
Eval or trace convention
Standardizes what every AI feature logs and reviews, so observability is consistent across teams instead of bespoke per project.
Cost visibility baseline
Shows spend by model, team, feature, environment, or request class. It makes AI cost a managed number before finance escalates it.
Internal developer workflow
Shows how engineers safely build, test, and release AI features on the platform. Adoption is the real test of a platform, so this is demonstrated early.
Rollout and ownership notes
Defines who owns the gateway, prompts, evals, incidents, provider changes, and cost reviews. Shared infrastructure fails without clear ownership.
Default stack
Stack fluency for AI Platform Engineer work.
The exact tools follow your environment. These are the common surfaces we vet against for this role.
Use cases
Where this hire creates leverage.
The best use case is one where the role can own a clear first proof during the paid trial.
Enterprise model gateway
Centralize provider access, routing, rate limits, and fallback rules so every team does not rebuild the same wrapper.
Prompt evaluation hub
Give teams a shared way to test prompt and model changes. Pair with an LLM Engineer who owns the eval methodology for a specific product.
Shared RAG layer
Avoid each product team rebuilding ingestion, retrieval, and context services. The proof is one service two teams can actually call.
Agent developer platform
Give teams reusable tooling for traces, approvals, and tool registries so agent work is consistent and reviewable.
Cost telemetry
Make spend visible by team and feature before leadership asks why the AI bill grew. The first proof is a working cost baseline.
AI observability
Trace requests, failures, latency, context, and model behavior across products from one place.
Self-serve AI platform
Let teams ship AI features without reinventing safety and infrastructure each time, behind a supported internal SDK.
Transparent pricing
Pick seniority by ownership, not mystery quotes.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Outcome clarity
What should change after you hire this role?
AI access is reusable.
Cost and traces are visible.
Teams can ship AI changes with safer release controls.
Adjacent-role comparison
When another AI role is the better hire.
LLM Engineer
Choose LLM Engineer if the focus is model behavior/evals for one product.
RAGRAG & Context Engineer
Choose RAG Engineer if retrieval quality is the bottleneck.
SECAI Security Engineer
Choose AI Security Engineer if governance/security is blocking release.
APPAI Application Engineer
Choose AI Application Engineer if the work is product feature delivery.
Vetting criteria
Screened for this role’s failure modes.
Platform API design
Provider routing
Observability
Cost controls
Release safety and governance
Interview questions
Use the interview to test judgment.
- How would you centralize model access without slowing teams?
- What should a model gateway enforce?
- How do you allocate AI cost by team or product?
- What trace convention should every AI feature use?
Hiring flow
From scope to paid trial.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Security, IP, governance
Repo access is scoped before the engineer starts.
NDA, IP assignment, repository access, communication channels, data boundaries, and AI tool rules are clarified before onboarding. Devlyn avoids unverified compliance claims and works within buyer-controlled systems.
FAQ
Questions before you hire AI Platform Engineer.
What does an AI Platform Engineer fix first?
Scattered model calls, missing cost visibility, weak eval gates, provider fallbacks, trace gaps, and release controls.
Is this MLOps?
It overlaps, but the focus is LLM-era delivery: gateways, eval hubs, RAG/agent services, observability, cost allocation, and internal developer experience.
When is this role premature?
If you have no proven AI use case yet, hire an app, RAG, LLM, or forward-deployed role first.
How fast can I see AI Platform Engineer candidates?
After the role scope, Devlyn targets two or three vetted profiles within 48 hours.
What does the two-week paid trial include?
The trial should produce role-specific proof for AI Platform Engineer work inside your actual repo, data environment, or approved workflow.
Can the engineer work in our repository?
Yes. Repo access, communication channels, data boundaries, NDA, and IP assignment are scoped before onboarding.
What if fit is wrong?
You can request a free replacement instead of being forced through a long lock-in or conversion fee.
What does pricing include?
Pricing covers one dedicated AI-native engineer. Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
Final CTA
Tell us the AI workflow. We’ll confirm whether AI Platform Engineer is the right hire.
If another role is a better fit, the role scope should catch that before you interview anyone.