AI Architect Roadmap
-Syntax Syndicate
A comprehensive, step-wise journey covering ML Engineering, MLOps, Generative AI, and Scalable Platforms (14 Months).
0
Phase 0: Setup & Foundations
Duration: 2 Weeks
Action Items
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Pick your stack:
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Study systems design for data/ML (focus on latency, throughput, SLOs, and cost per request).
Video Resource: ML System Design
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Watch the full course to grasp high-level architecture:
Deliverable
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Repo template with linting, tests, Makefile, and
1
Phase 1: Core ML Engineering
Duration: 0 – 2 Months
Action Items
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Refresh math: linear algebra, probability/statistics, optimization.
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Implement logistic regression, decision tree, and gradient boosting from scratch (using NumPy).
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Master core libraries: scikit-learn,
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Set up experiment tracking with
Video Resource: ML From Scratch
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Deep dive into implementation with this tutorial:
Project 1: Tabular ML Service
- Build a classic tabular ML project (e.g., fraud/credit/churn).
- Ship a FastAPI inference service packaged in a Docker image.
2
Phase 2: Data & MLOps Foundations
Duration: 2 – 4 Months
Action Items
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Master Data Engineering tools:
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Implement CI/CD for ML: unit tests for data & models, model registry, canary/blue-green deploys.
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Set up Cloud Infra using IaC:
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Establish Observability:
Project 2: End-to-End MLOps Pipeline
- Develop a training pipeline integrated with a feature store.
- Implement full CI/CD deployment to a Kubernetes cluster with autoscaling enabled.
3
Phase 3: Generative AI & LLMOps
Duration: 4 – 7 Months
Action Items
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Design RAG systems: chunking, embeddings, vector DBs, retrieval evaluation.
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Implement Prompt & Policy: guardrails, PII filtering.
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Control Latency/Cost: quantization, batching, caching.
Project 3: Production RAG Service
- Deliver a production-grade RAG service with evaluation and A/B testing capability.
4
Phase 4: Scalable Architectures
Duration: 7 – 10 Months
Action Items
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Master Architecture Patterns:
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Implement Security & Governance:
Project 4: Multi-Region AI API
- Build a multi-region, auto-failover AI API (using CDN + WAF).
- Define and thoroughly test RTO and RPO.
5
Phase 5: Platform & Enterprise Skills (Capstone)
Duration: 10 – 14 Months
Action Items
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Apply Platform Thinking:
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Address Compliance:
Project 5: AI Platform Starter Kit (Capstone)
- Deliver an "AI Platform Starter Kit" repository.
- One-click deploy of ETL → training → registry → serving → monitoring.
Career Acceleration Components
Certifications
- AWS Solutions Architect (Associate → Professional) OR Azure Architect Expert (AZ-305).
- CKA (Kubernetes).
- Terraform Associate.
- Optional: Databricks or GCP Professional ML Engineer.
Portfolio "Architect"
- 3 public reference architectures (diagrams + ADRs + costs + SLOs).
- RAG system with robust eval + safety filters + latency/cost charts.
- K8s-based serving stack with A/B & canary deploys, autoscaling, rollback playbooks.
- Cost-optimization case study.
Interview & Comp Playbook
- Systems-design drill: clarify reqs → constraints → designs → trade-offs → risks → mitigations.
- Behavioral: STAR stories for incidents, migrations, and cost reductions.
- Negotiate total compensation (base + bonus + RSUs).
Quick Start (Next 14 Days)
- Day 1–3: Spin up a managed Kubernetes cluster and deploy a toy FastAPI model with autoscaling configured.
- Day 4–7: Build a tiny RAG system using pgvector + OpenAI/Local LLM; add a basic offline evaluation harness.
- Day 8–14: Wire up MLflow + CI/CD for your toy model; add drift monitors; publish an ADR + diagram for your entire setup.