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Hire AI platform engineers who build the road every AI feature ships on.
TekRecruiter is an AI platform engineering recruiting agency focused solely on technology and engineering roles at tech and SaaS companies. We know platform work from feature work, and serving a model from calling one, so you hire engineers who make AI fast, affordable and safe for every team and agent that builds on it. Beyond skills and experience, we look for HEARTThe HEART standardHHigh agencyEExecutionAAccountabilityRResourcefulnessTTransparencyWhat we look for, beyond skills →.
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What is an AI platform engineer?
An AI platform engineer builds and runs the shared infrastructure a company’s AI products and agents depend on: model gateways and inference serving, GPU capacity, retrieval and vector infrastructure, evaluation and tracing pipelines, and the permissions and sandboxes that let agents act safely. Where an AI engineer builds one AI feature, an AI platform engineer builds the paved road every AI feature ships on.
- AI platform engineer
- Builds the shared infrastructure for AI: model gateways, inference serving, retrieval, evals, tracing and agent sandboxes.
- MLOps engineer
- Automates the lifecycle of custom-trained models: training pipelines, model registry, deployment and drift monitoring.
- Platform engineer
- Builds the internal developer platform: golden paths and self-service infrastructure that product teams build on.
What companies hire AI platform engineers to do.
Once a second team starts shipping AI, the same plumbing gets rebuilt twice. These are the six things an AI platform team builds once.
01 · Model gateway
One front door to every model.
A gateway that routes every AI request across providers and self-hosted models, with keys, quotas, fallbacks and cost tracking in one place.
- Routing by task, price and latency, with failover between providers
- Per-team budgets, rate limits and usage chargeback
- LiteLLM
- Portkey
- Envoy
- Kong
- Amazon Bedrock
- Azure OpenAI
02 · Inference and GPUs
Serve models fast without paying for idle GPUs.
Self-hosted inference for open-weight models: batching, quantization and autoscaling on GPU clusters sized to real traffic.
- Continuous batching and KV-cache tuning for throughput
- GPU scheduling and scale-to-zero on Kubernetes
- vLLM
- NVIDIA Triton
- TensorRT-LLM
- KServe
- Ray Serve
- Kubernetes
03 · Retrieval and streaming
Fresh context, delivered in milliseconds.
Vector and search infrastructure, embedding pipelines and event streams that keep what AI features retrieve current and permission-aware.
- Streaming updates from source systems into the indexes
- Access controls enforced at query time
- Kafka
- Flink
- pgvector
- Pinecone
- Elasticsearch
04 · Agentic platform
Give agents real work, inside real limits.
Sandboxes, scoped credentials and tool registries that let AI agents write, test and deploy code, or act in business systems, alongside engineers.
- Isolated execution environments with least-privilege access
- Audit trails and human approval on sensitive actions
- Model Context Protocol
- Firecracker
- gVisor
- Docker
- Vault
- Temporal
05 · Evals and tracing
See what every model and agent did, and why.
Shared evaluation pipelines, tracing and dashboards, so every team can measure quality, catch regressions and replay an agent run step by step.
- Evals in CI that block a regressing prompt or model change
- Traces that tie each answer to its inputs, retrieval and cost
- OpenTelemetry
- Langfuse
- Arize Phoenix
- Braintrust
- Datadog
06 · Cost and governance
AI spend you can forecast.
Caching, quotas and model-tier policies that hold AI unit costs steady, plus the data controls security and legal need before approving a new use case.
- Prompt and semantic caching on repeat traffic
- Data residency and retention rules applied by default
- Redis
- Kubecost
- Terraform
- AWS
- Google Cloud
What to know before you hire an AI platform engineer.
The title is new and used loosely. Settle these six questions and the search gets much easier.
When do you need an AI platform team?
When more than one team ships AI. The signs are easy to spot: each team holds its own model keys, runs its own vector database and writes its own evals, and nobody can say what AI costs per customer. Before that point, one AI engineer with strong infrastructure instincts can carry it.
What does an AI platform engineer cost?
In TekRecruiter’s 2026 placements, platform and SRE engineers building agentic AI platforms averaged a $190,000 base across 21 hires in Miami, New York, Boston and San Francisco, most of them hybrid with three days on site. Staff and principal roles run higher. See the 2026 Salary & Rate Guide.
AI platform engineer, MLOps engineer or platform engineer?
An MLOps engineer productionizes models you train yourself. A platform engineer builds the general developer platform. An AI platform engineer builds for foundation-model workloads: gateways, inference, retrieval, evals and agent safety. If you mostly call hosted models and run agents, you want the third, and it usually draws on platform or SRE experience plus AI workloads.
Which skills matter most?
Strong Kubernetes, cloud and distributed-systems fundamentals first, because an AI platform is still a platform. On top of that: inference serving and GPU scheduling, model gateways and routing, evaluation and tracing pipelines, and security for agents. Engineers from a software background tend to build tooling developers adopt; engineers from infrastructure tend to be stronger on GPU clusters and networking.
Contract or full-time for an AI platform build-out?
A contract engineer suits a defined stand-up: the first gateway, an eval pipeline or GPU serving for one model. Ongoing ownership needs a full-time engineer, because models, providers and prices change every few months and someone has to keep the platform current. Contract-to-hire covers both: build on contract, then convert.
How should you interview an AI platform engineer?
Ask for a platform other teams used. Who built on it, how much traffic it carried, and what a model call cost before and after their work. Then ask about a bad day: a provider outage, a rate limit hit in production, or an agent that did something it shouldn’t. The answers show whether they ran it or only set it up.
Why AI platform searches go wrong.
Few engineers have run AI infrastructure for other teams, and the title means something different at every company. These are the common misses.
- 01
An AI engineer hired for a platform job.
Strong at building features, but never ran shared infrastructure other teams depended on.
- 02
Platform skills, no AI workloads.
Knows Kubernetes well, but has never scheduled a GPU or served a model.
- 03
A title nobody agrees on.
MLOps, LLMOps, AI infrastructure and AI platform used for the same job, or for four different ones.
- 04
Access before guardrails.
Agents got tool access first; permissions and audit trails came after the incident.
- Agentic AI platform placements in 2026
- 21
- Average base salary across those placements
- $190K
- Miami, New York, Boston and San Francisco
- 4 markets
A platform proves itself in other teams’ hands.
So we ask every candidate who built on their platform, how much traffic it carried, what a model call cost before and after their work, and what happened the day a provider went down or an agent overstepped. Before sourcing, we separate AI platform work from AI feature work with you, because they draw on different engineers. Every candidate we introduce also meets HEART.
- Who built on it: the teams and agents using the platform, and what they shipped faster because of it.
- Serving and scale: gateway, routing, batching and GPU decisions, with the latency and cost numbers behind them.
- Agent safety: sandboxes, scoped credentials, approvals and audit trails for automated actions.
- Failure handling: provider outages, rate limits and runaway spend, and what they changed afterward.
| A generalist IT recruiting firm | TekRecruiter |
|---|---|
| Treats AI platform and AI feature work as one role | Separates platform from product AI before the search |
| Matches on Kubernetes plus an AI keyword | Asks for gateway, serving and GPU work they ran in production |
| Ignores what agents are allowed to do | Screens for permissions, sandboxes and audit trails |
| Borrows pay from DevOps or ML surveys | Prices from our 21 agentic AI platform placements |
| Searches one city | Recruits across Miami, New York, Boston and San Francisco |
The HEART standard
What we look for beyond skills and experience, in every candidate we present.
- High agencyPeople who see what needs to be done and act without waiting to be told.
- ExecutionPeople who turn ideas into results.
- AccountabilityPeople who own the outcome, not just their piece of the work.
- ResourcefulnessPeople who figure things out when the answer isn’t obvious.
- TransparencyPeople who communicate clearly, honestly, and early.
What we screen out.
The patterns that separate an engineer who runs AI infrastructure from one who has deployed a model once.
Never had internal users
Built AI infrastructure for one project, never a platform other teams relied on.
GPUs as a black box
Can’t discuss batching, memory or why utilization sat at 30%.
One provider, no fallback
Every request hard-wired to a single model API, with no routing or failover.
Evals left to each team
No shared evaluation or tracing, so nobody could compare quality across products.
Agents with standing credentials
Long-lived keys and broad access instead of scoped, short-lived permissions.
No cost per request
Knows the monthly bill, not what each feature, team or tenant costs.
Senior, staff and principal AI platform engineers.
Each level widens who depends on the engineer’s work: one service, several teams, then the whole company’s AI.
Senior
Owns a platform component in production.
- Runs the gateway, serving layer or eval pipeline end to end
- Carries the on-call for it and fixes the causes of incidents
- Onboards product teams and their agents
2026 base pay
~$190K
Average base, TekRecruiter’s 2026 agentic AI platform placements.
Staff
Owns the AI platform’s architecture.
- Chooses between hosted models and self-hosted inference, and owns the cost
- Sets the guardrail, eval and access standards every team follows
- Plans GPU capacity against the product roadmap
2026 base pay
~$210K–$245K
About 10–30% over senior, in our experience.
Principal
Sets the company’s AI infrastructure strategy.
- Multi-year bets on providers, open-weight models and hardware
- Advises leadership on AI cost, risk and compliance
- Aligns AI platform, security and data teams on one direction
2026 base pay
$210K–$300K
US range in our experience; depends on company and market.
Our 2026 agentic AI platform roles were mostly hybrid, three days on site. See the 2026 Salary & Rate Guide.
AI platform engineer vs. MLOps engineer vs. AI engineer.
| AI platform engineerYou are here | MLOps engineer | AI engineer | |
|---|---|---|---|
| Builds | Shared infrastructure for AI products and agents | Pipelines to train, deploy and monitor custom models | AI features and workflows on foundation models |
| Serves | Internal teams and their agents | Data scientists and ML engineers | End users |
| Typical work | Gateways, inference, GPUs, evals and tracing, agent sandboxes | Training pipelines, model registry, feature stores, drift monitoring | Retrieval, prompts, tool use and evals for one product |
| Often comes from | Platform engineering or SRE | ML engineering or data engineering | Software engineering |
| Hire when | Several teams ship AI and each rebuilds the same plumbing | You train your own models and need them reliable in production | You have an AI feature to build and ship |
How to hire AI platform engineers with us.
Direct hire
A permanent owner for the platform, since models, providers and prices keep changing. The placement carries a 90-day guarantee.
Direct hireStaff augmentation
A contract engineer to stand up the gateway, the first eval pipeline or GPU serving, starting in as little as three days.
Staff augmentationContract-to-hire
Start the build on contract, then convert the engineer your other teams already rely on.
Contract-to-hire
AI platform engineering questions, answered.
What is an AI platform engineer?
An AI platform engineer builds and runs the shared infrastructure that a company’s AI products and agents run on: model gateways, inference serving and GPU capacity, retrieval infrastructure, evaluation and tracing pipelines, and the permissions and sandboxes that keep agents safe. The role combines platform engineering with AI workloads.
How much do AI platform engineers make in 2026?
In TekRecruiter’s 2026 placements, platform and SRE engineers building agentic AI platforms averaged a $190,000 base across 21 hires. In our experience, staff engineers earn about 10–30% more than senior, and principal roles in the US run $210,000–$300,000. See the 2026 Salary & Rate Guide.
What is the difference between an AI engineer and an AI platform engineer?
AI engineers build AI into products and workflows: the retrieval, prompts, agents and evaluations behind a feature. AI platform engineers build the shared infrastructure those engineers and their agents run on: model gateways and serving, retrieval and evaluation infrastructure, GPU capacity, and the permissions and sandboxes that keep agents safe. Most companies add the platform role once more than one team ships AI.
What is the difference between an AI platform engineer and an MLOps engineer?
An MLOps engineer automates the lifecycle of models a company trains itself: training pipelines, the model registry, deployment and drift monitoring. An AI platform engineer serves foundation-model workloads across teams: gateways, inference, retrieval, evals and agent safety. Companies that train models and also run agents often need both.
When should a company build an AI platform team?
When more than one team is shipping AI and each is rebuilding the same pieces: model access, vector search, evals and cost tracking. A single AI platform engineer can often cover the first year; the team grows as agents get access to more systems.
Where do you recruit AI platform engineers?
Our 2026 agentic AI platform placements were spread across Miami, New York and Boston, with a smaller share in San Francisco. Most were hybrid, three days on site. We recruit on site, hybrid and remote.
Should an AI platform build-out be contract or full-time?
Contract works for a defined stand-up, such as the first model gateway or an eval pipeline, and a contract engineer can start in as little as three days. Full-time works for ongoing ownership. Contract-to-hire lets you do the first and convert to the second.
What is the HEART standard?
HEART is the standard we screen every candidate against, beyond skills: high agency, execution, accountability, resourcefulness and transparency. It describes people who take ownership, turn ideas into results and move the business forward. See the standard.
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