AI Platform Engineering

31 steps

AI Platform Engineering

Build and operate the shared delivery, ML, retrieval, agent, security, evaluation, and infrastructure systems behind production AI.

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What You Will Learn

Design a secure self-service platform for ML and generative AI workloads
Build reproducible model, retrieval, agent, and evaluation delivery systems
Operate accelerator infrastructure, model serving, observability, and cost controls
Threat-model and govern production AI systems
Deliver one evidence-backed production capstone

Prerequisites

Comfortable with Git, APIs, Linux, and one programming language
Basic understanding of containers and cloud infrastructure
Prior production software experience is strongly recommended

Core path

Learning content

Build the core capabilities before applying them in the practical work.

Engineering Foundations

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Define platform boundaries, operating planes, ownership, golden paths, and evidence loops.

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Delivery and Software Supply Chain

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Build verifiable delivery from reviewed source to immutable release and rollback evidence.

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Containers, Kubernetes, and Observability

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Operate secure, observable workloads with bounded resources and user-centered service objectives.

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Python for AI Services

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Design typed, tested, asynchronous AI service boundaries and provider adapters.

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ML Lifecycle and Reproducibility

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Connect problem framing, versioned data, experiments, evaluation, registry, and promotion.

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Model Delivery and Monitoring

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Package, release, monitor, scale, and roll back online and batch model workloads.

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Infrastructure, GitOps, and Internal Platforms

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Create reusable infrastructure, reconciled delivery, policy, and self-service golden paths.

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AI Platform Architecture

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Design control, data, and evidence paths with multi-tenancy and failure containment.

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Generative AI Systems

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Engineer context, model access, routing, structured responses, streaming, and evaluation.

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Retrieval Engineering

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Build authorized ingestion, hybrid retrieval, reranking, citations, and index operations.

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Agentic Systems

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Use bounded agent control loops, durable state, tool safety, and human approval.

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Context, Tools, and Protocols

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Design secure tools, memory lifecycles, context construction, and protocol boundaries.

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LLMOps and Evaluation

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Create continuous release evidence across prompts, models, retrieval, tools, quality, and cost.

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AI Security and Governance

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Threat-model AI systems and operate layered controls, red teaming, audit, and governance.

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Distributed AI Infrastructure

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Plan accelerator capacity, distributed execution, serving engines, and failure recovery.

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Model Customization

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Make evidence-based fine-tuning, preference optimization, adapter, and deployment decisions.

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Multimodal Platforms

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Operate document, image, audio, video, streaming, retrieval, and media evidence pipelines.

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AI Product Engineering

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Turn AI capability into an understandable, measurable, supportable, and sustainable product.

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Forward-Deployed Engineering

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Discover, scope, prototype, integrate, release, and support AI in customer environments.

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Apply the path

Applied work

Use the concepts in a workshop, project, or capstone and produce concrete evidence.

Capstone Portfolio and Production AI Platform

Integrate the complete path into a multi-tenant knowledge and operations platform.

Capstone 1Open

Capstone: Enterprise RAG Platform

Build authorized ingestion, retrieval, citation, evaluation, and index lifecycle.

Capstone 2Open

Capstone: AI Incident Response Agent

Build a bounded operations agent with evidence, budgets, approval, and durable execution.

Capstone 3Open

Capstone: AI Observability Control Plane

Connect production traces, quality, latency, failure analysis, and unit cost.

Capstone 4Open

Capstone: AI Security Testing Platform

Automate repeatable red-team testing and evidence-backed release gates.

Capstone 5Open

Capstone: Multi-Agent Research System

Coordinate evidence gathering, claim verification, contradiction handling, and citation.

Capstone 6Open

Capstone: AI Code Review Platform

Combine code context, deterministic analyzers, and high-precision model findings.

Capstone 7Open

Capstone: AI Customer Support Platform

Build cited support answers, customer tools, financial approvals, and human handoff.

Capstone 8Open

Capstone: AI Workflow Automation Platform

Combine durable workflows with bounded model extraction, classification, and review.

Capstone 9Open

Capstone: AI SRE Assistant

Analyze service objectives, capacity, and reliability evidence without bypassing change control.

Capstone 10Open

Capstone: Documentation Intelligence Platform

Detect stale technical documentation and produce evidence-backed updates through review.

Capstone 11Open

Capstone: Bounded Software Engineering Agent

Resolve a narrow issue class inside an isolated workspace with validation and review.

Capstone 12Open