AI Architect

Goal: Design the foundational architecture for an AI platform and make explicit architectural trade-offs.

1. Day01

   Define the major layers and responsibilities of a shared AI platform.

2. Day02

   Compare architectural options and justify choices using scale, reliability, latency, cost and team constraints.

3. Day03

   Design model serving components and explain scaling and isolation concerns.

4. Day04

   Design routing strategies for different models, workloads and quality/cost requirements.

5. Day05

   Design an LLM gateway for common controls such as routing, policy, logging and usage management.

Goal: Design the distributed infrastructure needed for reliable, asynchronous and scalable AI workloads.

1. Day06

   Package AI services consistently and explain deployment and isolation implications.

2. Day07

   Design asynchronous AI jobs using queues and workers to handle long-running workloads.

3. Day08

   Identify useful AI caching layers and design cache behavior for performance and cost.

4. Day09

   Design retries, timeouts, fallbacks and isolation boundaries for AI services.

5. Day10

   Design an event-driven AI workflow and explain when events are preferable to synchronous calls.

Goal: Architect a production knowledge platform with retrieval, planning and governance.

1. Day11

   Design a production RAG platform with ingestion, indexing, retrieval, generation and evaluation layers.

2. Day12

   Combine lexical and semantic retrieval and explain the trade-offs.

3. Day13

   Design query planning that selects retrieval strategies and execution steps based on the request.

4. Day14

   Define ownership, freshness, access, lineage and lifecycle controls for enterprise knowledge.

5. Day15

   Architect the knowledge platform mini project and defend its major components and trade-offs.

Goal: Move from individual agents to stateful, multi-agent and workflow-oriented platform architecture.

1. Day16

   Implement and validate the knowledge platform architecture.

2. Day17

   Harden the platform design based on observed quality, reliability and operational issues.

3. Day18

   Model agent execution as explicit states, transitions, conditions and recovery paths.

4. Day19

   Design when multiple specialized agents should collaborate and how coordination should work.

5. Day20

   Design deterministic and agentic workflow patterns and decide where each belongs.

Goal: Create platform-level controls for tools, memory and agent quality.

1. Day21

   Design policies for tool discovery, authorization, validation, rate limits and safe execution.

2. Day22

   Design memory layers, ownership, retrieval and lifecycle for agents.

3. Day23

   Design shared evaluation infrastructure for agent tasks, trajectories and regressions.

4. Day24

   Architect an agent platform use case with governance, memory and evaluation requirements.

5. Day25

   Implement the agent architecture and validate key platform decisions.

Goal: Design adaptation and multimodal/voice capabilities as platform concerns rather than isolated features.

1. Day26

   Refine the agent platform based on evaluation results and operational findings.

2. Day27

   Choose between prompting, RAG and model adaptation using product, data, quality and cost constraints.

3. Day28

   Design how parameter-efficient adaptation fits into model lifecycle and serving.

4. Day29

   Design shared platform components for text, image and other modalities.

5. Day30

   Design a low-latency voice AI architecture with streaming, turn-taking and failure handling.

Goal: Establish platform-wide security, evaluation, tracing, SRE and cost controls.

1. Day31

   Threat-model an AI platform and design controls against prompt injection, data leakage and tool abuse.

2. Day32

   Design shared evaluation pipelines, datasets, metrics and regression gates.

3. Day33

   Design end-to-end tracing across models, retrieval, tools, agents and user requests.

4. Day34

   Define reliability objectives, incident signals, failure handling and operational practices for AI systems.

5. Day35

   Design cost allocation, budgets, usage controls and architectural levers for AI economics.

Goal: Bring scalability, cloud architecture and platform design together in a defended capstone.

1. Day36

   Design a cloud architecture that can scale AI workloads while controlling reliability and cost.

2. Day37

   Produce an end-to-end production AI platform architecture with clear interfaces and operational controls.

3. Day38

   Present the capstone architecture and defend its major decisions under questioning.

4. Day39

   Implement key platform components and defend the resulting engineering and architecture choices.

5. Day40

   Document architecture decisions, lessons, trade-offs and portfolio-ready evidence of capability.