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ML Infrastructure & LLMOps Recruitment
Specialist search for the engineers who build and run the platform AI depends on: GPU clusters, training pipelines, inference and serving, and the observability that keeps models fast, reliable and affordable in production.
- GPU & compute platforms
- Inference & serving
- MLOps & LLMOps
Infra
that keeps models running
The discipline
What an ML infrastructure engineer actually does
An ML infrastructure engineer builds the platform that models are trained on and served from.
Where an ML engineer builds the model and an AI engineer builds the product on top of it, ML infrastructure engineers own everything underneath: compute and GPU scheduling, training and data pipelines, model registries and deployment, inference serving, and the monitoring that catches problems before users do.
LLMOps is the newest part of the discipline: serving large language models efficiently, managing prompts, evaluations and model versions as they change, and keeping inference cost and latency under control as usage grows. These engineers usually come from platform, infrastructure and SRE backgrounds, and those who have run ML workloads in production are in high demand.
Roles we recruit
ML infrastructure roles we place
From the engineers who keep GPU clusters busy to the leaders who own the platform every model team depends on.
01
ML Infrastructure Engineers
Build and run the compute layer for training and inference: GPU clusters, scheduling, storage and networking, and the tooling that lets model teams move quickly.
02
LLMOps & MLOps Engineers
Own the path from trained model to production: model registries, CI/CD for models, evaluation pipelines, versioning, rollout and rollback.
03
Inference & Serving Engineers
Make models fast and affordable to run: batching, quantization, caching, autoscaling and serving frameworks tuned for latency and throughput.
04
ML Platform Engineers
Build the internal platform data scientists and ML engineers use every day: feature stores, training orchestration, experiment tracking and self-service deployment.
05
Data & Training Pipeline Engineers
Build the pipelines that feed models: large-scale data processing, dataset versioning, distributed training jobs and the reliability work that keeps them running.
06
Heads of ML Infrastructure & Platform
Leaders who set platform strategy, decide build versus buy for compute and tooling, and keep cost, reliability and developer speed in balance as AI usage grows.
ML Infrastructure · What we screen for
Models are only as good as the platform they run on
How it differs
ML infrastructure vs ML engineering vs AI engineering
Three disciplines that are often confused, often combined in one job description, and hired from three different talent pools.
| AI Engineering | ML Engineering | ML Infrastructure & LLMOps | |
|---|---|---|---|
| What they build | Products on top of foundation models: LLM features, agents, retrieval | Models trained on your own data: ranking, prediction, fine-tuned models | The platform models train and run on: GPUs, serving, pipelines, observability |
| Core skills | Software engineering, context design, RAG, agents, evaluation, cost and latency | Statistics, training pipelines, feature engineering, fine-tuning, model serving | Distributed systems, GPU scheduling, inference optimization, MLOps tooling |
| Typical background | Strong software engineers who moved into LLM products | ML engineers, applied scientists and data scientists who ship | Platform, infrastructure and SRE engineers who specialized in ML |
| Screen for | Evaluation discipline, production failures debugged, cost reasoning | Experiment design, offline vs online metrics, models taken to production | Throughput, latency and cost wins delivered; incidents owned at scale |
| Hire first when | You're building LLM-powered features on foundation models | You have proprietary data and a prediction or ranking problem | Inference cost, latency or reliability is limiting the product |
| Typical titles | AI Engineer, LLM Engineer, Applied AI Engineer, Forward Deployed Engineer | ML Engineer, Applied Scientist, Research Engineer, Recommendation Engineer | ML Infrastructure Engineer, LLMOps / MLOps Engineer, ML Platform Engineer, Inference Engineer |
Discipline
AI Engineer Recruitment
LLM applications, agents, retrieval and evaluation on foundation models.
Discipline
Machine Learning Engineer Recruitment
Model training, fine-tuning, ranking and recommendation on your own data.
Our assessment
How we qualify ML infrastructure engineers
Strong infrastructure candidates can describe any architecture. We focus on what they ran, at what scale, and what it cost. We ask about:
A training or inference platform they built or ran, and how they kept GPUs utilized
A latency or cost problem in model serving, and what they changed to fix it
A production incident they owned, from detection through to the postmortem
How they shipped model updates safely: versioning, evaluation gates and rollback
Signals of real production experience
- Talks about cost per request, not just uptime
- Has run GPU workloads in production, not only in experiments
- Builds for the model teams who use the platform
- Can explain a serving optimization, with before-and-after numbers
FAQ
Hiring ML infrastructure engineers: common questions
What does an ML infrastructure engineer do?
They build and run the platform that machine learning depends on: compute and GPU scheduling, training and data pipelines, model deployment and serving, and the monitoring that keeps models reliable in production.
What's the difference between MLOps and LLMOps?
MLOps covers the lifecycle of any model: training pipelines, versioning, deployment and monitoring. LLMOps applies the same discipline to large language models, adding inference optimization for very large models, prompt and evaluation management, and close attention to serving cost.
When should we hire ML infrastructure before more ML or AI engineers?
When the bottleneck is no longer the model but running it: slow or expensive inference, unreliable training jobs, GPUs sitting idle, or model teams waiting on deployments. One strong platform hire can make the rest of the team noticeably faster.
How long does an ML infrastructure search take?
Typically two to three weeks to a shortlist and four to eight weeks to offer. Leadership roles and roles needing deep GPU or inference expertise usually take longer.
Do you recruit platform and SRE engineers without ML experience?
We focus on engineers who have run ML workloads in production. For general platform or SRE roles, we'll tell you upfront whether we're the right fit.
Wayne Lindor
PRINCIPAL | PRIZED
Wayne heads up the AI Engineering practice at Prized, recruiting across LLM applications, agents, retrieval and evaluation, machine learning and research, and the ML infrastructure underneath, up to and including VP Engineering, Head of AI, Chief AI Officer and CTO level.
Hiring for ML infrastructure? Let's talk
Tell us what you're building and where the gap is. A brief scoping call gives you a read on the market, an honest view of whether the role is hirable as written, and a clear next step.