Search Teardown: ML Platform Engineer for Production LLM Inference
This is a methodology example rather than a live candidate search. The goal is to show how a recruiter can turn a broad “AI/ML engineer” request into observable work without making every current AI keyword a hard requirement.
Last reviewed: 2026-09-06
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1. Rewrite the title into the work
Assume the hiring team asks for an “ML Platform Engineer with LLM experience.” That phrase is too broad to search responsibly. Translate it into the systems the person will own: model serving, inference performance, deployment, observability, GPU or accelerator utilization where relevant, reliability, and the interface between model teams and production infrastructure.
- Role family: ML Platform / ML Infrastructure / MLOps / Software Engineer - ML Systems
- Core evidence: production model serving and operational ownership
- LLM context: relevant only to the degree the production environment actually requires it
- Open question: model training depth versus serving/platform depth
2. Separate literal requirements from discovery vocabulary
Framework names, model families, vector databases, serving runtimes, CUDA, Kubernetes, Ray, Triton, vLLM, model gateways, and observability tools can all improve discovery. Unless the brief makes one of them non-negotiable, keep them as evidence/search expansions rather than screening gates.
This prevents the search from rejecting strong systems engineers who solved the same class of production problem with a different stack.
3. Build complementary evidence lanes
Run professional-history, GitHub, Hugging Face, technical writing, conference/talk, donor-company, and ATS rediscovery lanes independently enough to measure what each adds.
- Professional history: owned ML platform, model serving, distributed systems, SRE-style reliability
- GitHub: serving/infrastructure libraries, performance tooling, orchestration, observability
- Hugging Face: model/application artifacts when relevant
- Research: useful for specialized methods, but not required for a platform role
- Donor companies: organizations operating meaningful model/inference infrastructure
4. Rank for operating evidence rather than AI keyword count
A profile containing “GenAI, RAG, LLM, agents” is weak evidence if it does not show systems ownership. Look instead for deploying services, reducing latency/cost, scaling inference, debugging production behavior, capacity planning, platform APIs, or reliability responsibilities.
The candidate review should distinguish direct evidence, reasonable inference, and unanswered questions such as actual production scale.
5. Calibrate on the platform boundary
After the first slate, ask whether rejected candidates were too research-heavy, too application-layer, too SRE-generalist, or too MLOps-tooling-focused. That tells you which boundary is wrong.
Update the search plan explicitly rather than adding more AI buzzwords to the query.