What our mlops engineers do
Most AI projects stall between a working notebook and a production service. MLOps engineers close that gap with reproducible training, model registries, automated deployment, scaling and observability.
They also own the cost side of AI: right-sizing GPUs, batching and caching requests, and routing traffic between models so your AI features stay affordable as usage grows.
What you can build with our mlops engineers
- CI/CD pipelines for training, testing and deploying models
- Model registries and experiment tracking with MLflow or Weights & Biases
- Scalable model serving on Kubernetes, SageMaker or Vertex AI
- GPU clusters and autoscaling for LLM inference
- Monitoring for latency, errors, drift and spend
- Data and model versioning for audits and rollbacks
Flexible ways to hire
- Dedicated engineer: a full-time mlops engineer working only on your product, in your tools and sprints.
- Part-time or on demand: expert hours for reviews, architecture or a specific feature.
- Managed team: a small cross-functional team that delivers a defined project end to end.
