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SENIOR GENAI ENGINEERING · AWS + GCP · 2026 EDITION
Prepare like an engineer.
Answer like an owner.
A complete, visual-first preparation book for Senior GenAI, AI Platform, GenAI Solutions Architect, and Forward Deployed roles — LLM internals to agent platforms, with dedicated AWS and Google Cloud tracks.
LLM INTERNALSRAG & AGENTSEVALSBEDROCKVERTEX AILLMOPS
AIGenAI
PREPARED FOR
PURNENDU DAS
SENIOR GENAI · AWS + GCP
GenAI
Interview
Handbook
PREPARED FORPURNENDU DAS
16deep chapters
227interview drills
YOUR READING DASHBOARD
Build momentum, one proof at a time.
Progress and private notes stay in this browser.
researched words across standalone topic pages
diagrams, block architectures, and infographics
topic-specific questions with answer outlines
TABLE OF CONTENTS
The complete curriculum
CHAPTER 01Foundation
The Preparation Strategy
Build a senior GenAI positioning system: decode job descriptions, map signals, and ship proof artifacts that convert interviews into offers.
27 min14 drills6 diagramsRead →
CHAPTER 02Priority 0
LLM Internals, Serving & Inference Optimization
Master transformer anatomy, KV-cache math, continuous batching, quantization, speculative decoding, vLLM-class engines, and GPU economics on AWS and GCP.
32 min15 drills5 diagramsRead →
CHAPTER 03Priority 0
Prompting, Context Engineering & Model Adaptation
Engineer prompts, structured outputs, and context budgets; then climb the adaptation ladder — RAG, LoRA/QLoRA, preference tuning, distillation — with honest decision criteria.
26 min12 drills5 diagramsRead →
CHAPTER 04Priority 0
Retrieval, Vector Search & Production RAG
Master hybrid retrieval, HNSW, chunking, reranking, GraphRAG, multimodal RAG, and managed platforms — Bedrock Knowledge Bases, Vertex AI Search, and self-hosted engines.
36 min16 drills6 diagramsRead →
CHAPTER 05Priority 0
Agentic Systems & LLM Application Engineering
Design observable, cost-bounded agent workflows with MCP, durable execution, multi-agent topologies, approvals, and managed runtimes on Bedrock and Vertex AI.
34 min16 drills6 diagramsRead →
CHAPTER 06Priority 0
Evaluation & AI Quality Engineering
Create representative datasets, layered metrics, calibrated judges, agent trajectory evals, red teaming, regression gates, and production feedback loops.
33 min16 drills6 diagramsRead →
CHAPTER 07Priority 1
Enterprise Integrations & Backend Engineering
Build resilient APIs, OAuth flows, verified webhooks, idempotent workers, streaming LLM backends, and multi-provider LLM gateways with budgets and metering.
22 min12 drills6 diagramsRead →
CHAPTER 08Cloud track
The AWS GenAI Stack: Bedrock, SageMaker & Serverless AI
Go deep on Bedrock — Knowledge Bases, Agents, Guardrails, customization — plus SageMaker serving, vector options, IAM/VPC security, cost engineering, and reference architectures.
31 min14 drills6 diagramsRead →
CHAPTER 09Cloud track
The GCP GenAI Stack: Vertex AI, Gemini & Agent Builder
Go deep on Vertex AI — Gemini, Model Garden, Vector Search, RAG Engine, ADK and Agent Engine — plus BigQuery AI, GKE serving, VPC-SC security, and cost engineering.
37 min14 drills6 diagramsRead →
CHAPTER 10Priority 1
Data Systems, Cloud & Platform Engineering
Run PostgreSQL and pgvector with confidence; build embedding pipelines, CDC-driven indexing, lakehouse foundations, Kubernetes platforms, and GPU capacity plans.
22 min14 drills0 diagramsRead →
CHAPTER 11Priority 1
LLMOps, Reliability, Observability & Security
Trace every model call, monitor quality and cost in production, version prompts and models, defend against OWASP LLM risks, and lead AI incident response.
21 min14 drills0 diagramsRead →
CHAPTER 12Architecture
GenAI System Design & Forward Deployed Engineering
Practise complete GenAI architectures — copilots, document intelligence, LLM gateways — with token-level capacity math, AWS and GCP variants, and executive communication.
22 min14 drills0 diagramsRead →
CHAPTER 13Senior signal
Leadership, Coding & Communication
Strengthen GenAI interview coding — RAG pipelines, streaming handlers, eval harnesses — while preparing staff-level stories, design writing, and influence.
18 min14 drills0 diagramsRead →
CHAPTER 14Targeting
Role-to-Topic Preparation Map
Decode senior GenAI job descriptions — GenAI engineer, AI platform, LLMOps, solutions architect, FDE — into targeted chapter plans and proof artifacts.
18 min14 drills0 diagramsRead →
CHAPTER 15Execution
A Practical 12-Week Execution Sequence
Use a two-hour daily cadence to convert the 16-chapter curriculum into benchmarks, cloud reference builds, eval gates, design reps, and interview-ready stories.
19 min14 drills0 diagramsRead →
CHAPTER 16Final gate
Official References & Final Readiness
Work from primary sources — AWS, GCP, model providers, foundational papers — audit every proof artifact, score readiness honestly, and run full interview simulations.
21 min14 drills0 diagramsRead →
DEDICATED CLOUD TRACKS
Fluent on both clouds.
Senior GenAI loops probe cloud architecture. Both stacks get a full chapter plus mappings in every systems chapter.
AWS GenAI Track
Bedrock end to end — Knowledge Bases, Agents & AgentCore, Guardrails, customization — plus SageMaker serving, OpenSearch vectors, IAM/VPC security, and cost math.
- Enterprise RAG reference architecture
- Agentic platform on Bedrock
- Provisioned-throughput economics
Google Cloud GenAI Track
Vertex AI end to end — Gemini, Model Garden, Vector Search, RAG Engine, ADK & Agent Engine — plus BigQuery AI, GKE serving, VPC-SC, and cost engineering.
- Enterprise RAG reference architecture
- Agent platform with ADK
- Grounding & provisioned throughput
HOW TO USE THIS BOOK
Learn. Build. Break. Explain.
Senior interviews reward production judgment. Every chapter moves from durable mental models to diagrams, implementation, failure analysis, interview drills, and one piece of evidence you can defend.
- 01
Read for decisions
Mark the constraints that change the answer—not merely the vocabulary.
- 02
Build the proof artifact
Capture architecture, baseline, measurements, one failure, and your recommendation.
- 03
Rehearse from memory
Use the interview studio, answer aloud, then score structure and technical depth.
- 04
Attach your real evidence
Replace example numbers with honest project metrics and prepare follow-up detail.
ROLE-RELEVANT COVERAGE
Built around production GenAI—not trivia.
Model layer
Transformer internals, serving engines, quantization, GPU economics, adaptation and fine-tuning.
Application layer
RAG systems, agentic workflows, MCP, prompt and context engineering, structured outputs.
Quality layer
Golden sets, calibrated judges, trajectory evals, red teaming, release gates, online feedback.
Cloud layer
Bedrock and Vertex AI in depth, vector platforms, serverless AI backends, IAM and network security.
Operations layer
LLM observability, drift, prompt registries, OWASP LLM Top 10, incidents, cost guardrails.
Leadership layer
GenAI system design, FDE discovery, executive communication, staff-level stories, coding drills.
Research policy: volatile vendor and role claims are linked to primary sources and carry a checked date. Durable principles are separated from product-specific behavior.
Review sources & readiness →