AI Engineer: 6-Week Prep Path

Reading time: ~40 min | Interview relevance: Critical | Roles: AI Engineer, LLM Engineer, Applied AI Engineer, GenAI Engineer

The Real Interview Moment

The interviewer leans forward and asks: "A customer is seeing inconsistent answers from their RAG-powered support chatbot. Sometimes it hallucinates product features that don't exist. Sometimes it refuses to answer questions it should be able to handle. Walk me through how you would diagnose and fix this."

This is not a theoretical question. This is what AI Engineers deal with every single day. The role is new -- it barely existed before 2023 -- but it has become one of the fastest-growing positions in tech. Companies are desperate for engineers who can build reliable, production-grade AI applications using large language models.

The AI Engineer sits at the intersection of software engineering and applied AI. You do not need to train models from scratch, but you need to deeply understand how they work, how to prompt them, how to ground them with retrieval, how to build agent systems, and how to ship all of this reliably to production.

This 6-week plan will prepare you for the specific interview challenges of this emerging role.

Role Overview

What AI Engineers Do

AI Engineers build applications powered by large language models and other foundation models. They:

• Design and build RAG (Retrieval-Augmented Generation) systems
• Create AI agents with tool use and multi-step reasoning
• Engineer prompts and evaluation frameworks
• Integrate LLM APIs into production applications
• Optimize for latency, cost, and quality in AI systems
• Build evaluation pipelines and guard rails for AI outputs

Interview Format (Typical)

Round	Duration	Focus
Phone Screen	45-60 min	Coding + LLM concepts
Coding Round	45-60 min	Python, API design, async programming
LLM Deep Dive	45-60 min	Transformer internals, prompting, RAG, fine-tuning
System Design	60 min	LLM application architecture
Behavioral	45 min	AI product sense, ethics, collaboration

:::note The AI Engineer Interview is Still Evolving Unlike MLE or SWE interviews, the AI Engineer interview format is not yet standardized. Some companies focus heavily on coding, others on LLM knowledge, others on system design. Research your target company's process carefully. :::

Focus Area Allocation

Breakdown by Skill

LLM Focus (40% -- ~60 hours total)

• Transformer architecture: attention, positional encoding, generation strategies
• Prompting: zero-shot, few-shot, chain-of-thought, tree-of-thought
• RAG: chunking, embedding, retrieval, reranking, generation
• Agents: tool use, planning, multi-step reasoning, frameworks
• Fine-tuning: LoRA, QLoRA, when to fine-tune vs prompt
• Evaluation: LLM-as-judge, human evaluation, automated metrics

Coding (25% -- ~35 hours total)

• Python proficiency: async/await, generators, decorators
• API design: REST, streaming, webhooks
• LLM integration patterns: retry logic, rate limiting, fallbacks
• Data processing: chunking, embedding pipelines, vector operations

System Design (20% -- ~30 hours total)

• RAG architecture: end-to-end design at scale
• Agent systems: orchestration, state management, error handling
• LLM serving: inference optimization, caching, load balancing
• Cost optimization: token management, model routing, caching strategies

Behavioral (15% -- ~20 hours total)

• AI product sense: knowing when LLMs are the right solution
• Ethical AI: hallucination mitigation, bias, safety
• Project impact: quantifying improvements in AI systems
• Cross-functional collaboration with product and design teams

6-Week Schedule Overview

Week 1: Foundations -- LLM Internals and Python

Goal: Build deep understanding of how LLMs work and sharpen Python coding skills.

Daily time: 3.5 hours (weekdays), 5 hours (weekends)

Monday -- Transformer Architecture

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (strings, arrays)
Lunch (20 min)	Read	LLM Interviews overview
Evening (90 min)	Study	Self-attention mechanism: Q, K, V matrices, scaled dot-product attention, multi-head attention
Night (15 min)	Review	Write out the attention formula from memory

:::tip The Attention Equation You Must Know $\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$

Be ready to explain every component: why we scale by $\sqrt{d_k}$, what softmax does here, why we have separate Q/K/V projections, and how multi-head attention differs from single-head. :::

Tuesday -- Transformer Components Deep Dive

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (hash maps, two pointers)
Lunch (20 min)	Read	Positional encoding approaches
Evening (90 min)	Study	Layer normalization, feed-forward networks, residual connections, positional encodings (sinusoidal, RoPE, ALiBi)
Night (15 min)	Review	Draw the full transformer block architecture

Wednesday -- LLM Pre-Training and Architecture Variants

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium + 1 Python-specific problem (generators, async)
Lunch (20 min)	Read	GPT vs BERT vs encoder-decoder comparison
Evening (90 min)	Study	Causal language modeling, masked language modeling, next-token prediction, tokenization (BPE, SentencePiece)
Night (15 min)	Review	Compare autoregressive vs masked vs encoder-decoder architectures

Thursday -- Decoding Strategies and Generation

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (trees, graphs)
Lunch (20 min)	Read	Temperature, top-k, top-p sampling
Evening (90 min)	Study	Greedy decoding, beam search, nucleus sampling, temperature scaling, repetition penalties, logit bias
Night (15 min)	Review	Explain when to use each decoding strategy

Friday -- Python for AI Engineering

Time	Activity	Details
Morning (60 min)	Coding	2 coding problems focused on async/await patterns
Lunch (20 min)	Read	Coding Interviews Python focus
Evening (90 min)	Study	Async programming, streaming responses, generator patterns, rate limiting, retry with backoff
Night (15 min)	Review	Implement a retry wrapper with exponential backoff

Saturday -- LLM API Integration Patterns

Time	Activity	Details
Morning (2 hrs)	Implementation	Build a structured output extraction pipeline using an LLM API
Afternoon (2 hrs)	Study	OpenAI API, Anthropic API, streaming, function calling, structured outputs
Evening (1 hr)	Practice	Implement error handling: timeouts, rate limits, content filtering, partial responses

Sunday -- Week 1 Review

Time	Activity	Details
Morning (2 hrs)	Review	Re-explain transformer architecture without notes
Afternoon (2 hrs)	Coding	Implement multi-head attention from scratch in Python
Evening (1 hr)	Plan	Identify weak areas; adjust Week 2 focus

:::note Week 1 Milestone Checkpoint

• Explain the transformer architecture end-to-end
• Compare autoregressive vs encoder-only vs encoder-decoder models
• Implement multi-head attention from scratch
• Write async Python code for LLM API calls with proper error handling
• Explain tokenization (BPE) and why it matters
• Describe 5+ decoding strategies with trade-offs :::

Week 2: Foundations -- Prompting, Embeddings, and RAG Basics

Goal: Master prompting techniques and understand the RAG pipeline end-to-end.

Daily time: 3.5 hours (weekdays), 5 hours (weekends)

Monday -- Prompt Engineering Fundamentals

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Prompt engineering best practices
Evening (90 min)	Study	Zero-shot, few-shot, chain-of-thought, system prompts, prompt templates, output formatting
Night (15 min)	Practice	Write 5 prompts for different tasks and evaluate outputs

Tuesday -- Advanced Prompting Techniques

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (DP, backtracking)
Lunch (20 min)	Read	Recent prompting research (tree-of-thought, self-consistency)
Evening (90 min)	Study	Tree-of-thought, self-consistency, ReAct, prompt chaining, meta-prompting
Night (15 min)	Review	Create a prompting technique decision tree

Wednesday -- Embeddings Deep Dive

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Word2vec vs contextual embeddings
Evening (90 min)	Study	Sentence embeddings, embedding models (OpenAI, Cohere, open-source), similarity metrics, embedding dimensions trade-offs
Night (15 min)	Review	Implement cosine similarity from scratch

Thursday -- Vector Databases and Retrieval

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	ANN algorithms: HNSW, IVF, PQ
Evening (90 min)	Study	Vector databases (Pinecone, Weaviate, Chroma, pgvector), indexing strategies, hybrid search, metadata filtering
Night (15 min)	Review	Compare vector databases in a feature matrix

:::warning Vector Search is Not Magic Many AI Engineer candidates treat vector search as a black box. Interviewers will ask about ANN algorithm trade-offs (recall vs speed), when to use hybrid search (keyword + semantic), and how to handle the "lost in the middle" problem. Understand the retrieval layer deeply. :::

Friday -- RAG Pipeline Architecture

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (string processing)
Lunch (20 min)	Read	RAG pipeline overview
Evening (90 min)	Study	End-to-end RAG: document ingestion, chunking strategies, embedding, retrieval, reranking, generation, citation
Night (15 min)	Review	Draw the full RAG pipeline architecture

Saturday -- Build a RAG System

Time	Activity	Details
Morning (2.5 hrs)	Implementation	Build a complete RAG pipeline: load docs, chunk, embed, store, retrieve, generate with citations
Afternoon (1.5 hrs)	Study	Chunking strategies: fixed-size, recursive, semantic, document-aware
Evening (1 hr)	Evaluate	Test your RAG system with different query types; identify failure modes

Sunday -- Week 2 Review

Time	Activity	Details
Morning (2 hrs)	Review	Refine your RAG implementation; add reranking
Afternoon (2 hrs)	Study	Fine-tuning overview: when to fine-tune, LoRA, QLoRA, data requirements
Evening (1 hr)	Practice	Explain RAG architecture to an imaginary interviewer (time yourself: aim for 10 minutes)

:::note Week 2 Milestone Checkpoint

• Apply 5+ prompting techniques appropriately to different tasks
• Build a working RAG pipeline from scratch
• Explain chunking strategies and their trade-offs
• Compare 4+ vector databases with specific use cases
• Describe the full embedding pipeline (model selection, dimensions, indexing)
• Explain when to use RAG vs fine-tuning vs prompt engineering :::

Week 3: Core Skills -- Agents, Tool Use, and Advanced RAG

Goal: Master agent architectures and advanced RAG patterns.

Daily time: 4 hours (weekdays), 5 hours (weekends)

Monday -- AI Agents: Fundamentals

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Agent architectures overview
Evening (120 min)	Study	ReAct pattern, function/tool calling, planning strategies, agent loops, state management
Night (15 min)	Review	Draw the ReAct agent loop

Tuesday -- Multi-Agent Systems

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Multi-agent frameworks (CrewAI, AutoGen, LangGraph)
Evening (120 min)	Study	Agent orchestration, delegation, communication patterns, consensus, error handling in multi-agent systems
Night (15 min)	Review	Compare single-agent vs multi-agent approaches

Wednesday -- Advanced RAG Patterns

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Advanced RAG techniques
Evening (120 min)	Study	Query decomposition, HyDE, self-querying retrieval, multi-step RAG, corrective RAG, graph RAG
Night (15 min)	Review	List 5 RAG failure modes and their solutions

:::tip Know These Advanced RAG Patterns Interviewers love asking about RAG edge cases. Be ready to discuss:

• Query decomposition: Breaking complex queries into sub-queries
• HyDE: Generating hypothetical documents to improve retrieval
• Corrective RAG: Validating retrieved documents before generation
• Graph RAG: Using knowledge graphs to enhance retrieval
• Multi-hop RAG: Answering questions that require information from multiple documents :::

Thursday -- LLM System Design: Chatbot Architecture

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	ML System Design framework
Evening (120 min)	Practice	Design a production chatbot: conversation management, context windows, memory, guardrails, fallbacks
Night (15 min)	Review	List the key components of a production chatbot

Friday -- Evaluation and Guardrails

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	LLM evaluation frameworks
Evening (120 min)	Study	LLM-as-judge, RAGAS metrics (faithfulness, relevance, context recall), human evaluation, output guardrails, content filtering
Night (15 min)	Review	Design an evaluation pipeline for a RAG system

Saturday -- Build an Agent System

Time	Activity	Details
Morning (2.5 hrs)	Implementation	Build a tool-using agent: web search + calculator + code execution
Afternoon (1.5 hrs)	Study	Error handling in agents: retry, fallback, graceful degradation, timeout
Evening (1 hr)	Mock	First coding mock interview (45 min, focus on LLM integration code)

Sunday -- Week 3 Review

Time	Activity	Details
Morning (2 hrs)	Review	Refine agent implementation; add memory and conversation history
Afternoon (2 hrs)	Practice	Design an AI-powered code review system (system design practice)
Evening (1 hr)	Behavioral	Start drafting STAR stories for 3 AI projects

:::note Week 3 Milestone Checkpoint

• Build a working tool-using agent from scratch
• Explain ReAct, function calling, and planning patterns
• Describe 5+ advanced RAG techniques with use cases
• Design a production chatbot architecture with all components
• Implement an evaluation pipeline for a RAG system
• Compare agent frameworks (LangChain, LlamaIndex, custom) with trade-offs :::

Week 4: Core Skills -- Fine-Tuning, Optimization, and System Design

Goal: Understand fine-tuning workflows and LLM system design at production scale.

Daily time: 4 hours (weekdays), 5 hours (weekends)

Monday -- Fine-Tuning: When and How

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium/hard
Lunch (20 min)	Read	Fine-tuning decision framework
Evening (120 min)	Study	When to fine-tune vs RAG vs prompting, data curation for fine-tuning, instruction tuning, SFT
Night (15 min)	Review	Create a decision tree for fine-tuning vs RAG vs prompting

Tuesday -- Parameter-Efficient Fine-Tuning

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium/hard
Lunch (20 min)	Read	LoRA paper summary
Evening (120 min)	Study	LoRA, QLoRA, adapter layers, prefix tuning, prompt tuning -- how each works and when to use them
Night (15 min)	Review	Explain LoRA rank and its impact on model capacity

Wednesday -- LLM Serving and Optimization

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium/hard
Lunch (20 min)	Read	LLM inference optimization techniques
Evening (120 min)	Study	KV cache, continuous batching, speculative decoding, quantization (GPTQ, AWQ, GGUF), model distillation
Night (15 min)	Review	Calculate the memory footprint of a 7B parameter model at different precisions

:::danger Memory Calculation You Must Know For a model with N parameters:

• FP32: N x 4 bytes (a 7B model needs ~28 GB)
• FP16/BF16: N x 2 bytes (~14 GB)
• INT8: N x 1 byte (~7 GB)
• INT4: N x 0.5 bytes (~3.5 GB)

Plus KV cache memory, which grows with sequence length and batch size. This is a very common interview question. :::

Thursday -- System Design: RAG at Scale

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium/hard
Lunch (20 min)	Read	Production RAG architectures
Evening (120 min)	Practice	Design a RAG system for a legal document platform: 10M documents, sub-second latency, high accuracy requirements
Night (15 min)	Review	Identify bottlenecks in your design

Friday -- System Design: AI Agent Platform

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium
Lunch (20 min)	Read	Agent platform architectures
Evening (120 min)	Practice	Design a customer service agent platform: multi-turn conversations, tool integration, human handoff, compliance
Night (15 min)	Review	Compare your design with real-world examples (Intercom, Zendesk AI)

Saturday -- Cost Optimization and Caching

Time	Activity	Details
Morning (2.5 hrs)	Study	Semantic caching, prompt caching, model routing (small model for easy queries, large for hard), token budgeting
Afternoon (1.5 hrs)	Practice	Design a cost-optimized LLM serving layer with model routing
Evening (1 hr)	Mock	System design mock: design an AI-powered search engine (45 min)

Sunday -- Week 4 Review

Time	Activity	Details
Morning (2 hrs)	Review	Create summary sheets for fine-tuning and optimization
Afternoon (2 hrs)	Paper reading	Read one influential LLM paper (e.g., "Retrieval-Augmented Generation for Knowledge-Intensive Tasks")
Evening (1 hr)	Behavioral	Add 2 more STAR stories focused on technical decision-making

:::note Week 4 Milestone Checkpoint

• Explain LoRA mechanics and when to use parameter-efficient fine-tuning
• Calculate LLM memory requirements at different precisions
• Design a RAG system handling 10M+ documents with sub-second latency
• Describe 3+ LLM inference optimization techniques
• Design a cost-optimization strategy for an LLM serving platform
• Explain KV cache and continuous batching :::

Week 5: Polish -- Paper Discussion, Take-Homes, and Mock Interviews

Goal: Practice paper presentations, complete a take-home, and intensify mock interviews.

Daily time: 4 hours (weekdays), 5 hours (weekends)

Monday -- Paper Discussion Practice

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (company-tagged if possible)
Lunch (20 min)	Read	Paper Discussion guide
Evening (120 min)	Study	Read and annotate "Attention Is All You Need" -- prepare a 10-minute verbal summary
Night (15 min)	Practice	Present your summary to a mirror or recording

Tuesday -- RLHF and Alignment

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium/hard
Lunch (20 min)	Read	RLHF overview
Evening (120 min)	Study	RLHF pipeline: SFT, reward modeling, PPO. Also DPO, constitutional AI, red-teaming
Night (15 min)	Review	Explain the RLHF pipeline from SFT to deployment

Wednesday -- Take-Home Project

Time	Activity	Details
Morning (60 min)	Coding	1 LeetCode medium
Lunch (20 min)	Read	Take-Home Projects best practices
Evening (120 min)	Project	Start a mock take-home: build a RAG-powered Q&A system with evaluation
Night (15 min)	Plan	Outline remaining work

Thursday -- Take-Home Completion

Time	Activity	Details
Morning (60 min)	Coding	1 LeetCode medium
Lunch (20 min)	Read	Code quality for AI projects
Evening (120 min)	Project	Complete take-home: add evaluation metrics, error handling, documentation, clear README
Night (15 min)	Self-review	Critique the project as if you were the reviewer

Friday -- Company Research

Time	Activity	Details
Morning (60 min)	Coding	2 company-tagged problems
Lunch (20 min)	Read	Company Guides for target companies
Evening (120 min)	Research	Target company AI products, tech blog, engineering culture, recent launches
Night (15 min)	Notes	Write company-specific talking points

Saturday -- Full Mock Interview Day

Time	Activity	Details
Morning (2 hrs)	Mock	Coding mock (45 min) + LLM deep dive mock (45 min)
Afternoon (2 hrs)	Mock	System design mock: design an AI writing assistant (60 min)
Evening (1 hr)	Debrief	Catalog all weaknesses and create a remediation plan for Week 6

Sunday -- Week 5 Review

Time	Activity	Details
Morning (2 hrs)	Review	Revise all system designs done so far
Afternoon (2 hrs)	Study	Safety and ethics: hallucination mitigation, prompt injection, jailbreaking, content filtering
Evening (1 hr)	Behavioral	Practice all STAR stories; add one focused on AI safety/ethical decision-making

:::note Week 5 Milestone Checkpoint

• Present a 10-minute paper summary clearly and confidently
• Complete an AI take-home project in under 4 hours with evaluation metrics
• Explain RLHF, DPO, and constitutional AI
• Discuss prompt injection attacks and mitigation strategies
• Have 6+ polished STAR stories ready
• Know your target company's AI products and tech stack :::

Week 6: Final Week -- Simulations, Review, and Confidence

Goal: Final mock interviews, targeted review, and mental preparation.

Daily time: 3 hours (weekdays), 5 hours (weekends)

Monday -- Light Coding and Review

Time	Activity	Details
Morning (60 min)	Coding	2 LeetCode medium (comfortable patterns)
Lunch (20 min)	Read	Negotiation and Offers
Evening (60 min)	Review	Skim all cheat sheets: transformers, RAG, agents, fine-tuning, system design
Night (15 min)	Relax	Light reading or rest

:::warning Do Not Learn New Topics in Week 6 The final week is for reinforcement, not acquisition. If you encounter something completely new, note it down but do not deep-dive. Confidence comes from mastery of what you know, not anxiety about what you do not. :::

Tuesday -- Full Loop Simulation

Time	Activity	Details
Morning (60 min)	Warm-up	1 easy problem
Afternoon (3 hrs)	Mock	Full interview simulation: coding (45 min) + LLM deep dive (45 min) + system design (60 min) + behavioral (30 min)
Evening (30 min)	Debrief	Note top 3 strengths and top 3 areas to shore up

Wednesday -- Targeted Weakness Review

Time	Activity	Details
Morning (60 min)	Study	Deep review of your weakest area from Tuesday's mock
Lunch (20 min)	Review	Flashcards for key concepts
Evening (90 min)	Practice	3 targeted practice problems in your weak area
Night (15 min)	Review	Mental rehearsal of confident responses

Thursday -- Behavioral Final Prep

Time	Activity	Details
Morning (60 min)	Practice	Deliver all STAR stories aloud, timed (3-4 minutes each)
Lunch (20 min)	Read	Behavioral tips for final polish
Evening (90 min)	Mock	Final behavioral mock + "tell me about yourself" (60-second version)
Night (15 min)	Prep	Write 5 questions to ask your interviewers

Friday -- Rest and Logistics

Time	Activity	Details
Morning (30 min)	Logistics	Confirm schedule, test A/V setup, prepare water and snacks
Afternoon	Rest	Exercise, socialize, recharge
Evening (30 min)	Light review	One final skim of cheat sheets

Saturday -- Optional Light Practice

Time	Activity	Details
Morning (2 hrs)	Light	2 easy/medium problems for flow
Afternoon (2 hrs)	Review	Walk through one RAG system design slowly
Evening (1 hr)	Visualization	Mentally walk through each interview round

Sunday -- Full Rest

No studying. Sleep well. You have put in the work.

:::note Week 6 Final Assessment

• Can explain transformer architecture, RAG, and agent systems fluently
• Can code LLM integration patterns with proper error handling
• Can design an LLM-powered system in 45 minutes using a clear framework
• Can discuss fine-tuning vs RAG vs prompting trade-offs with nuance
• Can deliver behavioral stories naturally and confidently
• Can discuss AI safety, ethics, and hallucination mitigation
• Have questions prepared for interviewers that show genuine curiosity :::

Key Topics Quick Reference

LLM Concepts You Must Know Cold

Topic	Key Points	Common Questions
Transformer architecture	Attention, FFN, residuals, layer norm	"Walk me through how a transformer processes a sequence"
Tokenization	BPE, SentencePiece, token limits	"Why does the model struggle with arithmetic?"
Decoding strategies	Temperature, top-k, top-p, beam search	"How would you make outputs more/less creative?"
RAG pipeline	Chunk, embed, retrieve, rerank, generate	"Design a RAG system for X"
Fine-tuning	LoRA, QLoRA, SFT, when vs RAG	"When would you fine-tune vs use RAG?"
Agents	ReAct, tool calling, planning, memory	"Design an agent that can do X"
Evaluation	Faithfulness, relevance, LLM-as-judge	"How would you evaluate this system?"
Safety	Prompt injection, hallucination, guardrails	"How do you prevent hallucinations?"
Optimization	KV cache, quantization, batching	"How would you reduce latency by 50%?"
Cost	Token management, caching, model routing	"How would you handle $10K/month budget?"

System Design Problems to Practice

1. RAG-powered customer support chatbot -- Multi-turn, knowledge base integration, human handoff
2. AI writing assistant -- Real-time suggestions, style control, plagiarism prevention
3. Code review agent -- Multi-file analysis, tool use, explanation generation
4. Document intelligence platform -- PDF processing, structured extraction, summarization at scale
5. AI-powered search engine -- Semantic search, query understanding, result generation
6. Content moderation system -- Multi-modal, real-time, policy-configurable
7. Conversational analytics agent -- Natural language to SQL, chart generation, data exploration

Essential Resources

Handbook Chapters to Prioritize

Priority	Chapter	When to Study
Critical	LLM Interviews	Weeks 1-5
Critical	Coding Interviews	Weeks 1-4
High	ML System Design	Weeks 3-5
High	Behavioral	Weeks 5-6
Medium	Deep Learning	Weeks 2-4
Medium	Paper Discussion	Week 5
Medium	Company Guides	Week 5
Low	Take-Home Projects	Week 5
Low	Negotiation	Week 6

External Resources

Essential reading:

• "Attention Is All You Need" (Vaswani et al., 2017)
• "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020)
• Anthropic's research on constitutional AI and RLHF
• LangChain and LlamaIndex documentation

Tools to be familiar with:

• LangChain / LangGraph
• LlamaIndex
• Vector databases (Pinecone, Weaviate, Chroma)
• Inference servers (vLLM, TGI, Ollama)
• Evaluation frameworks (RAGAS, DeepEval)

Next Steps

You now have a complete 6-week roadmap for AI Engineer interview preparation. If this path does not match your target role, consider:

• MLE Prep Path -- If your role requires more traditional ML model training and classical ML
• MLOps Prep Path -- If your role emphasizes LLM infrastructure and deployment over application building
• Data Engineer Prep Path -- If your role focuses on the data infrastructure that feeds AI systems

The AI engineering field moves fast. Start your prep today, because the role you apply for in 6 weeks may have evolved since you started reading this page.