Expertise · AI infrastructure recruiting
MLOps and Model Serving Recruiting
We recruit MLOps and model serving engineers: training pipelines and artifact lifecycle, distributed training operations, inference infrastructure, latency and cost engineering, and forward-deployed engineers for customer deployments. Many of these roles get written up as data science jobs. The daily work is infrastructure, so we screen for infrastructure experience.
Audience
We work with AI product companies, model and research organizations, GPU cloud platforms, and enterprises running models in production with service targets.
Common hiring problems
- Model teams cannot ship because pipeline and serving work has no owner.
- Nobody can explain the inference cost per request, and nobody owns bringing it down.
- The requisition reads like data science and returns candidates who cannot operate infrastructure.
- Customer deployments need engineers who can work in the customer environment.
Role families
Roles and screening
MLOps and pipelines
MLOps engineer, ML infrastructure engineer, training infrastructure engineer
Pipeline and artifact lifecycle, distributed training operations, checkpointing and GPU utilization ownership.
Model serving and inference
Inference platform engineer, model serving engineer, performance engineer
Batching, quantization tradeoffs, cache behavior, tail latency and cost per request.
LLMOps and evaluation infrastructure
LLMOps engineer, evaluation infrastructure engineer
Prompt and model versioning, evaluation harnesses, and safe rollout of model changes.
Forward-deployed and solutions engineering
Forward-deployed engineer, solutions engineer, deployment engineer
Customer-facing judgment and infrastructure depth. We source them separately from internal platform engineers.
Environments
Technologies and environments
- Distributed training with checkpoint and restart discipline
- Serving stacks, batching and accelerator-aware inference
- Model and artifact registries, versioning and rollout
- Evaluation harnesses and regression gates
- Cost and latency attribution across tenants and endpoints
Role scope
Requirement mistakes
Sort these out before outreach starts.
- Scoping an infrastructure role as a data science role and screening on modeling depth.
- Combining internal platform work with customer-facing deployment work in one role.
- Requiring one serving framework and screening out engineers with strong accelerator performance experience on another.
- Hiring for inference cost work without giving the role authority over the serving stack.
Process
Search process
Czarina Tabayoyong agrees on the scorecard with your interview panel, builds a list of named candidates and contacts them directly. You get a written report every week. At the end, your team keeps all the research and the candidate pipeline. We use AI tools for research, and Czarina makes the recommendations herself.
Talent Intelligence Sprint
A fixed-scope research project on the candidate market before you commit to a search. Scopes are listed on the homepage.
Embedded Search Sprint
A recruiter inside your process for a defined sprint.
Site Launch or Team Build
Monthly recruiting support timed to your project milestones.
FAQ
Recruiting questions
- What is the difference between an AI engineer and an AI infrastructure engineer?
- An AI engineer builds product features on top of models. An AI infrastructure engineer makes the compute, pipelines and serving stack available, reliable and affordable. The resumes look similar, and we screen each for different experience.
- Is MLOps a data science hire or an infrastructure hire?
- In practice it is an infrastructure hire with model literacy. The daily work is pipelines, artifacts, scheduling, utilization and failure recovery.
- Do you recruit inference performance specialists?
- Yes. We screen them on work they can measure: tail latency, batching strategy, quantization tradeoffs and cost per request on specific hardware.
- What about forward-deployed engineering?
- Yes, as its own search. The role needs infrastructure experience plus the judgment to work inside a customer environment.
Next step
Bring the scorecard and the deadline. We'll tell you what the candidate market looks like and which engagement we'd recommend, including when your team can fill the role internally.