Personal roadmap from Senior GenAI Developer to AI Architect
Learn the whole AI architecture stack, then prove it in public.
This site turns your roadmap PDF into a study cockpit: foundations, ML systems, cloud platforms, RAG, agents, Kubernetes, governance, cost, observability, interview drills, and portfolio projects.
Today
Start with one clear move
You do not need to become everything at once. Each study block should make you stronger at one architect question: what business goal, what data path, what model strategy, what serving layer, what controls, and what cost envelope?
Production GenAI delivery
RAG, agents, AWS Bedrock, OpenSearch, Lambda, Pinecone, BigQuery, Airflow, Docker, CI/CD, business-impact metrics, and teaching content.
Architect-level public proof
Kubernetes, IaC, feature stores, model governance, inference optimization, observability, classical ML system design, and multi-cloud trade-offs.
Choose, govern, scale
Show that you can select the model, retrieval pattern, hosting layer, monitoring stack, security controls, and operating model for a business goal.
Study Coach
A friendly weekly plan you can actually follow
Pick the amount of time you can protect this week. The plan below keeps the rhythm simple: learn one concept, build one small artifact, explain one trade-off, and save one piece of public proof.
Choose this week's pace
This week
4-hour focus plan
Learn
Pick one topic from the A-Z map and write a plain-English explanation in your own words.
Build
Turn the explanation into a tiny artifact: diagram, notebook, Terraform sketch, eval script, or README.
Explain
Record the trade-off: why this design, what it costs, what can fail, and how you would operate it.
Publish
Save the proof in a repo or portfolio note so a hiring manager can see the architect signal.
Learning Path
Study in the order interviews test you
Each module has three outputs: understand the concepts, implement a working artifact, and turn the artifact into a hiring signal.
Foundation Reset
4-6 weeks
Refresh statistics, ML theory, embeddings, attention, evaluation, and optimization. You should be able to explain bias-variance, regularization, calibration, precision, recall, F1, ROC-AUC, PR-AUC, and why accuracy alone can be misleading.
- Rebuild intuition with regression, classification, trees, SVMs, clustering, and dimensionality reduction.
- Explain transformer basics: tokens, attention, positional encoding, encoders, decoders, and embeddings.
- Practice deriving business metric choices from model metric choices.
Classical ML and Deep Learning Systems
6-8 weeks
Design tabular, ranking, NLP, and time-series pipelines. Learn how data leakage, feature drift, training-serving skew, and threshold tuning affect production outcomes.
- Build one XGBoost or LightGBM tabular project with a clean feature pipeline.
- Build one recommendation, ranking, or fraud-style design with online scoring.
- Document model choice, feature choice, validation strategy, and failure modes.
MLOps and LLMOps Lifecycle
6-8 weeks
Move from notebooks to systems: experiment tracking, model registry, CI/CD/CT, lineage, eval sets, environment promotion, rollback, and reproducibility.
- Track experiments and prompts with MLflow or equivalent lifecycle tooling.
- Create promotion gates: tests, eval thresholds, approval, canary, rollback.
- Version data, prompts, models, features, APIs, and infrastructure together.
Cloud-Native AI Platforming
8-10 weeks
Learn the platform layer: IAM, KMS, VPCs, private connectivity, storage, queues, eventing, containers, Kubernetes, Helm, Terraform/CDK, and deployment substrate trade-offs.
- Defend Cloud Run/ECS vs GKE/EKS using security, scale, GPU, and operability constraints.
- Write one Terraform or CDK reference stack for an AI workload.
- Practice rolling, canary, blue-green, and shadow deployments.
Advanced LLM Systems
6-8 weeks
Master RAG, hybrid retrieval, reranking, context packing, agents, tool use, prompt injection defenses, PEFT, LoRA/QLoRA, batching, caching, and routing.
- Separate deterministic workflows from agentic workflows, and justify autonomy only where it adds value.
- Measure retrieval quality, faithfulness, latency, cost, and answer usefulness separately.
- Use smaller models for routing, classification, safety, and cost-sensitive steps.
Governance, Reliability, and FinOps
4-6 weeks
Add the controls architects are paid to own: model cards, risk registers, audit trails, PII handling, access controls, SLOs, incident playbooks, token budgets, and cost dashboards.
- Map each AI system to risks, mitigations, owners, and review cadence.
- Instrument logs, metrics, traces, drift, quality, latency, and token cost.
- Practice explaining reliability without hiding behind tool names.
A-Z Capability Map
Every topic you need to become interview-ready
Architecture Trade-offs
Decide managed platform vs open stack, sync vs async inference, serverless vs Kubernetes, and monolith vs services.
Prove it: write trade-off memos for each capstone.Business and Product Value
Tie AI choices to ROI, SLA, compliance, user workflow, risk tolerance, and measurable business outcomes.
Prove it: start every README with business problem to result.Cloud-Native AI
Design on AWS and GCP using IAM, networking, storage, observability, queues, and managed AI services.
Prove it: diagram one AWS and one GCP deployment.Data Engineering
Build batch and streaming pipelines with data contracts, schema governance, quality checks, and lineage.
Prove it: ship a feature pipeline with validation gates.Evaluation
Separate model, retrieval, faithfulness, latency, cost, safety, agent, offline, and online metrics.
Prove it: create a golden eval set and regression dashboard.Feature Stores
Understand offline vs online stores, point-in-time joins, freshness, reuse, ownership, and skew prevention.
Prove it: implement Feast or a managed feature-store demo.Governance
Use lineage, model cards, risk registers, access controls, approvals, human review, and audit trails.
Prove it: publish governance artifacts beside code.Human Feedback
Design annotation, HITL review, red-teaming, escalation, and product feedback loops.
Prove it: add a feedback schema and review workflow.Inference Optimization
Practice batching, quantization, caching, routing, autoscaling, GPU use, and latency budgeting.
Prove it: benchmark vLLM, TGI, or Triton with cost notes.Jobs and Orchestration
Use Airflow, Step Functions, SageMaker or Vertex pipelines, Kubeflow, and event-driven workflows.
Prove it: show retry, backfill, and failure handling.Kubernetes and Containers
Know Docker, pods, deployments, services, ingress, Helm, GPU scheduling basics, autoscaling, and canaries.
Prove it: deploy a model service to GKE or EKS.Learning Theory
Refresh statistics, optimization, bias-variance, calibration, regularization, and validation strategy.
Prove it: maintain short notes with interview answers.MLOps and LLMOps
Implement CI/CD/CT, experiment tracking, model registry, prompt registry, reproducibility, rollback, and promotion.
Prove it: automate build, test, eval, deploy.Networking and Security
Understand VPC design, private connectivity, KMS, IAM, secrets, egress control, isolation, and least privilege.
Prove it: publish threat model and network diagram.Observability
Instrument logs, metrics, traces, model drift, feature skew, retrieval quality, token cost, and incidents.
Prove it: add OpenTelemetry traces and dashboards.Privacy, PEFT, and Prompting
Cover PII handling, data minimization, guardrails, prompt injection defenses, LoRA, QLoRA, and adapter strategy.
Prove it: compare RAG vs fine-tuning vs PEFT.Query and Retrieval
Master chunking, BM25 plus dense retrieval, reranking, context packing, grounding, and vector-store trade-offs.
Prove it: benchmark retrieval pipelines on one corpus.Reliability and Responsible AI
Design fallback behavior, circuit breakers, safe failure modes, explainability, bias monitoring, and abuse resistance.
Prove it: write incident and rollback playbooks.Scalability
Plan concurrency, queues, backpressure, throughput, multi-region assumptions, capacity envelopes, and rate limits.
Prove it: load test and publish capacity numbers.Testing
Add unit, integration, data, prompt, regression, retrieval, model, security, and end-to-end tests.
Prove it: make tests block unsafe releases.User Experience
Design confidence signaling, source display, human escalation, error states, and task-focused AI interfaces.
Prove it: build a usable demo, not only an API.Versioning
Version data, prompts, features, models, adapters, APIs, eval sets, and infrastructure in one release story.
Prove it: trace one prediction back to all inputs.Workflow and Agents
Know when deterministic workflows beat agents, and when agent autonomy, tool use, and planning add value.
Prove it: compare workflow, agent, and hybrid designs.Explainability
Use model cards, attribution, rationale reporting, feature importance, decision transparency, and user-facing limitations.
Prove it: publish one model card and one explanation view.YAML and IaC
Produce reproducible infrastructure with Terraform, CDK, Helm values, deployment manifests, and environment configs.
Prove it: create one-click infrastructure setup.Zero-Trust and Zero-Downtime
Build secure-by-default systems that rotate secrets, roll forward, roll back, and survive component failure.
Prove it: document recovery time and blast radius.Answer Templates
A softer way to sound senior in interviews
When a question feels huge, use a calm structure. These templates keep your answer organized and make your judgment easy to hear.
When choosing a platform
I start with constraints: team maturity, compliance, latency, scale, cost, portability, and operational ownership. Then I choose the simplest platform that satisfies those constraints.
When moving to production
I add versioning, reproducible pipelines, test gates, evaluation thresholds, registry promotion, canary deployment, monitoring, rollback, and clear owners.
When improving answer quality
I separate retrieval quality from generation quality: chunking, metadata, hybrid search, reranking, context packing, citations, faithfulness checks, and human review.
When asked about governance
I name the risks, map owners, define controls, log evidence, set review cadence, and make releases depend on measurable readiness rather than trust.
Portfolio Proof
Four capstones that make the architect story visible
These are not tutorial projects. Each one should include architecture diagrams, trade-off notes, tests, dashboards, threat model, cost estimate, and a short demo.
Multi-cloud regulated RAG platform
Build document ingestion, PII classification, chunking, hybrid retrieval, reranking, grounded generation, guardrails, audit logs, eval sets, and cost controls.
- Stack: Bedrock or Gemini, OpenSearch or BigQuery/vector search, MLflow, OpenTelemetry, Terraform.
- Evidence: grounded-answer rate, citation quality, latency, token cost, red-team results.
Real-time fraud platform with feature store
Build streaming transaction ingestion, online feature serving, offline training, point-in-time joins, model registry, canary scoring, and drift monitoring.
- Stack: Kafka or Pub/Sub, Feast or managed feature store, XGBoost/LightGBM, MLflow, GKE/EKS.
- Evidence: skew checks, threshold policy, investigator workflow, rollback plan.
K8s inference optimization lab
Compare serving engines, autoscaling choices, batching, caching, quantization, cold starts, GPU utilization, and cost per 1,000 requests.
- Stack: Triton, vLLM or TGI, Helm, GKE/EKS, load testing, cost dashboard.
- Evidence: benchmark table, latency budget, failure modes, rollout strategy.
AI governance cockpit
Build a lightweight governance interface for model cards, risk registers, approvals, eval evidence, red-team findings, lineage, and release readiness.
- Stack: metadata store, policy checks, dashboard, audit log, OTel instrumentation.
- Evidence: risk lifecycle, review cadence, approval gate, incident playbook.
Whiteboard Playbooks
Answers you should be able to draw from memory
Regulated RAG
Separate offline ingestion from online inference. Ingestion handles extraction, PII redaction, chunking, embeddings, metadata, and indexes. Online serving uses hybrid retrieval, reranking, prompt building, guarded generation, citations, audit, metrics, and feedback review.
Real-time Fraud
Stream transactions, enrich with customer and account data, compute online features, score under a latency target, log decisions, retrain with point-in-time joins, and monitor drift, skew, bias, thresholds, and investigator outcomes.
Cloud Run/ECS vs GKE/EKS
Use serverless containers for stateless APIs and fast delivery. Use Kubernetes when you need custom networking, sidecars, GPU-aware scheduling, advanced autoscaling, or a standardized internal platform across teams.
Credential Path
Certifications that support the pivot
Certifications are not the goal; they are signal boosters for the portfolio proof. Prioritize architect and production ML credentials over beginner badges.
Priority
Certification
Why it matters
High
Google Professional Cloud Architect
Validates solution design, security, compliance, business process optimization, and operations excellence.
High
Google Professional Machine Learning Engineer
Supports production ML, model development, deployment, monitoring, and platform fluency on GCP.
High
AWS Certified Machine Learning Engineer - Associate
Signals ability to implement and operationalize ML workloads in production on AWS.
Medium
AWS Certified Generative AI Developer - Professional
Adds a focused GenAI production signal for foundation model integration and business workflows.
Interview Conversion
Drills for architect-level confidence
Why use a feature store?
Emphasize point-in-time correctness, reuse, online/offline consistency, lineage, ownership, freshness, and skew monitoring.
LoRA vs full fine-tuning vs QLoRA?
Discuss cost, memory, quality, update frequency, serving implications, adapter management, and data risk.
How do you evaluate a RAG bot?
Separate retrieval recall, citation quality, groundedness, answer usefulness, latency, cost, safety, human review, and A/B tests.
Design a model registry and deployment workflow.
Cover lineage, artifacts, eval gates, approvals, environment promotion, canary, rollback, monitoring, and ownership.
Design an LLM observability stack.
Include traces, prompt versions, chain steps, retrieval spans, feedback, red-team results, cost, latency, and incident dashboards.
Secure production agent tool use.
Use least privilege, scoped tools, approval gates, sandboxing, prompt-injection controls, audit logs, and rate limits.
Influenced architecture without authority.
Show stakeholder alignment, decision records, data-backed trade-offs, compromise, and durable operating change.
When AI was the wrong answer.
Prove judgment: simpler workflow, rules, search, analytics, or UX change beat model complexity.
Readiness Checklist
Before applying seriously to AI Architect roles
Treat this like a calm scoreboard. You are not behind; you are making the invisible parts of your capability visible.
Source-Grounded Resources
Study anchors
Google Practitioners Guide to MLOps
Google CI/CD/CT for ML systems
Amazon SageMaker AI documentation
Feast feature store documentation
OpenTelemetry documentation
NIST AI Risk Management Framework
OWASP Top 10 for LLM Applications
An Introduction to Statistical Learning
Stanford CS229
Deep Learning Book