AI EngineeringCurriculum in development
LLMOps Engineer
Deploy and operate language models with reliable serving, telemetry, scaling, security, and rollback.
Designed forPlatform, DevOps, MLOps, and AI engineers responsible for production model infrastructure.
Recommended foundationLinux, containers, networking, cloud infrastructure, and basic model-inference concepts.
Problems you will learn to solve
Work from real constraints, not generic tool demonstrations.
Serve models within latency and throughput targets
Detect regressions after a model release
Scale GPU capacity without losing cost control
Working environment
Docker, Kubernetes, vLLM, KServe, Envoy AI Gateway or LiteLLM, Kueue, GPUs, Prometheus, OpenTelemetry, cloud
System mapA production model platform
Model registry
Release pipeline
Inference gateway
Model serving
GPU capacity
Metrics + traces
Scale + rollback
Provisional curriculum
Six connected modules
The sequence will be validated with practitioners before enrollment opens.
- 01Inference architecture
- 02Model artifacts and registries
- 03GPU serving and batching
- 04Release and rollback pipelines
- 05Telemetry and reliability
- 06Security, capacity, and cost
Planned capstone
Finish with evidence of applied skill.
A production model platform with controlled releases, autoscaling, telemetry, security, and rollback.
Focused lessonsUnderstand the underlying ideas
Guided practiceWork through realistic constraints
Applied projectProduce a demonstrable result
Structured reviewRevise the work after feedback
Program updates
Register your interest in LLMOps Engineer.
This is not enrollment and no payment is required. We will use your response to validate demand and contact you when the curriculum and cohort details are ready.