Hire anAI Infrastructure Engineer
The platform under your AI: gateways, GPUs, vector stores, and zero-downtime scale.
Builds and runs the substrate AI products live on, model gateways, inference infrastructure, vector databases, caching, rate limiting, and multi-provider failover.
30-minute fit call with an engineering lead. No hard sell, no deposit, and no change to the published rates.
OUTAGES YOUR CUSTOMERS NEVER SEE
WHAT THEY OWN
Concrete deliverables, not job-description poetry.
01
Model gateway & routing layer
One controlled door to every provider, keys, quotas, fallbacks, audit.
02
Inference infrastructure
Self-hosted or hybrid serving tuned for latency and unit cost.
03
Vector store operations
Indexing, sharding, and backup strategies that survive growth.
04
Caching & rate limiting
Semantic caching and throttling that cut spend without cutting quality.
05
Provider failover
Outages at OpenAI or Anthropic become a log line, not an incident.
06
Observability foundation
Latency, cost, and error budgets per feature, per customer.
HOW MATCHING WORKS
From role brief to production evidence.
You are not buying a resume. You are choosing a specific engineer, then testing the match on committed work before making a longer decision.
01
Map the real gap
A 30-minute call with an engineering lead defines what the AI Infrastructure Engineer must own, the stack they inherit, and the evidence that will count as a successful trial.
02
Review matched profiles
Within 48 hours, you receive 2-3 role-matched profiles. You can review the work history, technical evidence, certifications, and availability before choosing whom to interview.
03
Interview against the work
We help turn your current failure cases into practical interview scenarios. You choose the engineer; no profile moves forward without your approval.
04
Prove fit in one paid week
The engineer works in your repo or approved data environment. You judge real output, communication, and technical decisions before any month-to-month continuation. Typical evidence includes mapped every model call path in your product, put a gateway in front of the chaos, broke down cost per feature and per customer.
What makes a shortlist useful: each profile should match the ownership boundary, not merely repeat the right tools. Compare the candidate's recent work, the decisions they owned, the evidence they can explain, and the overlap they can commit to. Ask who reviewed the work and what changed after it reached production. Certifications support that judgment when a platform or security standard matters; they do not replace production experience.
No deposit, no unpaid test project, and no long-term contract required. Trial work is paid at the published rate and belongs to you.
PRICING
Pick the level, keep the senior oversight.
Junior
$3,200 /month
or $20/hr on Time & Material
AI-native from day one
Senior
MOST HIRED$4,800 /month
or $30/hr on Time & Material
Architecture & judgment
Dedicated engineers are billed monthly; Time & Material is billed hourly on tracked actuals. The paid one-week trial applies to every dedicated hire.
YOU NEED THIS ROLE IF
One provider outage takes your product down with it
AI spend is a single scary invoice nobody can decompose
Every team calls model APIs their own creative way
BY END OF WEEK ONE
Mapped every model call path in your product
Put a gateway in front of the chaos
Broke down cost per feature and per customer
Set the first latency and error budgets
OUTCOMES YOU CAN MEASURE
Provider outages your customers never see
AI unit economics per feature
Latency budgets that hold at scale
One governed path to every model
DEVELOPER PROFILES
See the depth behind a useful shortlist.
Explore AI Infrastructure Engineer profiles with the work evidence, relevant experience, and skill detail behind a useful shortlist.
Vinay
Senior AI Infrastructure Engineer
Forward-Deployed Engineer who turns AI prototypes into production features from inside client teams.
Python
TypeScript
Claude / GPT APIs
React+8
Ram
Senior AI Infrastructure Engineer
Senior AI Infrastructure Engineer with 13 years of experience, specializing in high-performance training clusters for autonomous systems.
Kubernetes
Terraform
NVIDIA GPUs
Linux+6
Chitrang
Senior AI Infrastructure Engineer
Senior AI Infrastructure Engineer with 8 years of experience, specializing in procurement and supplier agents for manufacturing.
KubernetesTerraformNVIDIA GPUsLinux+6
Anand
AI Infrastructure Engineer
LLM Engineer focused on agent reliability: prompts, RAG, evals, and structured outputs that survive production, not just the demo.
Raj
AI Infrastructure Engineer
AI Infrastructure Engineer with 6 years of experience, specializing in inference platform reliability for financial software.
KubernetesTerraformNVIDIA GPUsLinux+6
Rohan
AI Infrastructure Engineer
AI Infrastructure Engineer with 6 years of experience, specializing in AI infrastructure FinOps for marketplace technology.
KubernetesTerraformNVIDIA GPUsLinux+6COMMON QUESTIONS
What teams ask before they shortlist.
What does an AI Infrastructure Engineer own?
Builds and runs the substrate AI products live on, model gateways, inference infrastructure, vector databases, caching, rate limiting, and multi-provider failover. The role is accountable for concrete production deliverables, including model gateway & routing layer, inference infrastructure, vector store operations. The trial scope names the output, reviewer, and acceptance evidence before work starts.
How do I know whether we need an AI Infrastructure Engineer?
This role is usually the right hire when one provider outage takes your product down with it; aI spend is a single scary invoice nobody can decompose; every team calls model APIs their own creative way. On the matching call, an engineering lead checks the boundary against adjacent roles so you do not hire an impressive title for the wrong bottleneck.
How does Devlyn verify AI Infrastructure Engineer skills?
Profiles show relevant work history, technical interview evidence, role-specific capabilities, and meaningful certifications where they exist. We then help you interview against your own architecture and failure cases. The final check is a paid one-week trial in your repo or approved data environment, not a generic coding puzzle.
What should the paid one-week trial produce?
The trial is scoped around committed work your team already needs. For this role, a useful first week can include mapped every model call path in your product; put a gateway in front of the chaos; broke down cost per feature and per customer; set the first latency and error budgets. You keep the work whether or not the engagement continues.
What does it cost to hire an AI Infrastructure Engineer?
Published dedicated rates start at $3,200 per month, or $20 per hour for Time & Material work. Senior rates are $4,800 per month or $30 per hour. The trial is paid at the same published rate, with no deposit or conversion fee.
What happens if the engineer is not the right fit?
You can stop after the paid trial or request a free replacement during the engagement. Work continues month to month with no long-term lock-in. NDA and IP assignment are completed before onboarding, access is scoped to the work, and everything produced belongs to you.
PAIRS WELL WITH