From AI Builder to AI Platform Engineer

This bridge is for learners who can build AI applications with generative AI, RAG, embeddings, fine-tuning, or agentic workflows and want to operate AI infrastructure for other teams. It closes the gap between app-level AI skill and platform-level responsibility for Kubernetes operations, GPU scheduling, model serving, multi-tenancy, data infrastructure, governance, cost control, and reliability.

Diagnostic — Are You Ready?

• You have served an LLM behind concurrent traffic and measured latency, throughput, saturation, and error rate.
• You can explain the difference between prompt latency, prefill time, decode time, queue time, and total request latency.
• You know what NVLink topology means for distributed training and multi-GPU placement.
• You have debugged a CUDA OOM or can explain the memory impact of batch size, sequence length, KV cache, and model weights.
• You can describe GPU quotas, priority classes, taints, tolerations, and scheduling constraints in Kubernetes.
• You have operated a vector database or retrieval system beyond a single-user demo.
• You can explain how model serving differs from ordinary stateless HTTP serving.
• You can describe when KServe, Seldon, vLLM, Triton, and custom serving stacks are appropriate.
• You can explain why multi-tenant AI workloads need isolation across data, models, secrets, GPUs, and network paths.
• You know how training data lineage, model registry, approval, rollback, and audit trails affect production AI systems.
• You can identify cost drivers for inference, fine-tuning, batch jobs, and training clusters.
• You can define ownership boundaries between app teams, data teams, ML teams, and platform teams.

Skills Gap Map

What you have	What you need	Where to study it
AI application development	Distributed systems thinking for shared platforms	Distributed Systems
Prompt and RAG experience	Reliability targets for model-serving workloads	Reliability Engineering
Single-service deployment	AI infrastructure as a platform discipline	AI Infrastructure
Model experimentation	MLOps governance and lifecycle management	MLOps
Basic container use	Kubernetes workload scheduling and isolation	CKA
API integration	Production inference serving patterns	KServe
Local model serving	High-throughput LLM serving operations	vLLM
GPU awareness	GPU-aware scheduling and runtime constraints	AI Infrastructure
Vector search usage	Data platform reliability and scale	MLOps
Cloud API consumption	Private infrastructure and on-premises constraints	On-Premises AI/ML Infrastructure

Sequenced Path

1. Start with Distributed Systems. Why this step: AI platforms are distributed systems with shared bottlenecks, retries, queues, placement constraints, and partial failure.

2. Continue with Reliability Engineering. Why this step: model serving needs explicit SLOs for latency, freshness, availability, correctness boundaries, and degradation behavior.

3. Study AI Infrastructure. Why this step: this is the discipline layer that connects GPUs, schedulers, serving runtimes, data paths, isolation, and operating models.

4. Study MLOps. Why this step: platform engineers must support model lifecycle, experiment tracking, registry workflows, approvals, rollout, rollback, and audit.

5. Use KServe when you need Kubernetes-native model-serving patterns. Why this step: KServe exposes how inference services map to autoscaling, revisions, routing, runtimes, and production deployment concerns.

6. Use vLLM when LLM throughput is the central constraint. Why this step: vLLM makes memory layout, batching, KV cache behavior, and serving efficiency visible.

7. Use Triton when serving heterogeneous model types. Why this step: Triton is useful when the platform must support multiple frameworks, accelerators, batching strategies, and model formats.

8. Add On-Premises AI/ML Infrastructure if private GPU infrastructure is the target. Why this step: private AI infrastructure adds procurement, power, cooling, network fabric, shared storage, and hardware lifecycle constraints.

9. Return to Platform Engineering once the technical pieces are clear. Why this step: the platform only succeeds when teams can consume it through reliable golden paths and clear support boundaries.

Anti-patterns

• Assuming OpenAI API integration skill transfers directly to operating LLM serving infrastructure.
• Ignoring GPU memory accounting until production traffic arrives.
• Building inference systems without rate-limit, queue-depth, saturation, and tail-latency instrumentation.
• Treating vector databases as ordinary CRUD stores without recall, freshness, indexing, and capacity planning.
• Letting every team invent its own model-serving path.
• Treating governance as a document instead of a workflow embedded in model lifecycle tooling.

What success looks like

• You can explain how a model request moves through routing, queuing, batching, GPU execution, and response streaming.
• You can set GPU quotas and scheduling constraints that prevent one team from starving another.
• You can define serving SLOs that include latency, availability, saturation, and degradation behavior.
• You can choose between KServe, Seldon, vLLM, Triton, and custom serving based on platform requirements.
• You can connect model registry, rollout, rollback, observability, and audit needs into one operating model.
• You can design AI infrastructure that application teams can consume without becoming platform experts.

First module to read

Start with Platform Disciplines: AI Infrastructure.