Expertise · AI infrastructure recruiting
MLOps and Model Serving Recruiting
Critical Path Hiring recruits MLOps and model serving engineers: training pipeline and artifact lifecycle ownership, distributed training operations, inference infrastructure, latency and cost engineering, and forward-deployed engineering for customer-facing deployments. An AI engineer is not an AI infrastructure engineer, and this market is frequently mis-scoped as data science when the work is infrastructure work.
Who this is for
AI product companies, model and research organizations, GPU cloud platforms, and enterprises putting models into production behind real service targets.
The hiring problems it addresses
- Model teams cannot ship because pipeline and serving work has no owner.
- Inference cost per request is unexplained and no one owns reducing it.
- 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
What we recruit, and how each pool is screened.
MLOps and pipelines
MLOps engineer, ML infrastructure engineer, training infrastructure engineer
Screened on pipeline and artifact lifecycle, distributed training operations, checkpointing and GPU utilization ownership.
Model serving and inference
Inference platform engineer, model serving engineer, performance engineer
Assessed on batching, quantization tradeoffs, cache behavior, tail latency and cost per request.
LLMOps and evaluation infrastructure
LLMOps engineer, evaluation infrastructure engineer
Evaluated on prompt and model versioning, evaluation harnesses, and safe rollout of model changes.
Forward-deployed and solutions engineering
Forward-deployed engineer, solutions engineer, deployment engineer
Assessed on customer-facing judgment as well as infrastructure depth, which is a different hiring market from internal platform work.
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
Calibration
Common role-calibration mistakes.
Each of these costs a hiring cycle, and each is fixable 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 seat.
- Requiring a specific serving framework instead of accelerator-aware performance reasoning.
- Hiring for inference cost work without giving the role authority over the serving stack.
How we work
Founder-led search, written research, client-owned pipeline.
Czarina Tabayoyong calibrates the scorecard with the people who will run the panel, maps the named market, runs direct outreach, reports in writing every week, and transfers the research and pipeline to your team. AI assists. People decide.
FAQ
Buyer 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. They read as similar on paper and are separate talent markets with different evidence.
- 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. This is a small pool assessed on measurable work: tail latency, batching strategy, quantization tradeoffs and cost per request on specific hardware.
- What about forward-deployed engineering?
- We treat it as its own market. The role needs infrastructure credibility plus the judgment to work inside a customer environment, and mixing it into a platform requisition is a common reason those searches stall.
Have a role that keeps returning the wrong candidates?
Bring the scorecard and the deadline. You get a market read and a recommended engagement, even if the answer is to solve it internally.