Engineering
Applied AI Engineer
From research to production — reliably, at scale
Specializes in taking models from research or prototype into production. MLOps, model serving, fine-tuning pipelines, evaluation harnesses, drift detection. Bridges the gap between what the data scientists built and what the business can actually run.
What this role covers
Production hardening — Error handling, fallbacks, retries, observability, alerting
Model serving — Deployment, versioning, A/B testing, rollback strategies
Fine-tuning pipelines — Domain adaptation, RLHF, evaluation harnesses
Drift detection — Monitoring model performance over time, catching degradation
Cost optimization — Inference budgets, quantization, caching, batching strategies
When you need this role
Companies post-proof-of-concept
"We have a demo that blew everyone away. We have no idea how to run it for real customers. The latency is unacceptable. The cost is 40x what we expected."
Research-heavy orgs, academia-to-industry
"We have genuinely novel models. We have zero production engineering culture. We need someone who understands the research but can build the infrastructure."