All roadmaps
AI Engineering Track
Mentoxis AI Engineering Interview Roadmap
A start-to-offer path for the AI / LLM engineer interview in 2026 — from Python and LLM APIs to RAG, agents, evals and production, in the order that compounds.
- Stages
- 12
- Core
- 10
- Duration
- 21–26 weeks
Built for software engineers and ML-adjacent developers moving into applied AI / GenAI / LLM engineering roles.
1Core · sequential◇Optional · parallel∞Ongoing
Why it matters · Applied AI is software engineering with a probabilistic core. Loops break on weak Python, async, and API basics long before the model matters.
Core Python
- Pythonic idioms & comprehensions
- Type hints & static typing
- Data structures & dataclasses
- Error & exception handling
Async & concurrency
- async / await & the event loop
- asyncio: tasks, gather, queues
- Streaming responses
- Rate limits & retries
Data tooling
- Pandas & NumPy
- JSON & schema handling
- Pydantic for I/O contracts
Engineering basics
- Git & REST APIs
- FastAPI
- Docker basics
- Env vars & secrets
Mock this roundTutorials coming soonWhy it matters · You build on models, you don't train them. Interviewers expect you to explain tokens, embeddings, context windows and why a model hallucinates — not derive backprop or tune a loss function.
Language models 101
- What an LLM is & how it predicts
- Pretraining vs post-training (concept only)
- Base vs instruct vs reasoning models
Transformers & tokens
- Attention, at a high level
- Tokens & tokenization
- Embeddings & vector space
- Context windows
Model behaviour
- Why models hallucinate
- Temperature & randomness
- Strengths, limits & failure modes
Model landscape
- Open vs closed models
- Parameter count & quantization trade-offs
- Picking a model for the job
Mock this roundTutorials coming soonWhy it matters · Every AI-engineering loop starts here: calling models well, controlling cost and latency, and getting reliable structured output under pressure.
Working with LLM APIs
- OpenAI / Anthropic / open models
- Params: temperature, top-p, max tokens
- Streaming
Prompt engineering
- System prompts
- Few-shot prompting
- Chain-of-thought
- Decomposition
Structured output
- JSON mode & schemas
- Tool / function calling
Cost & reliability
- Token accounting
- Caching
- Retries & fallbacks
- Context-window management
Mock this roundTutorials coming soonWhy it matters · RAG is the pattern most applied-LLM job listings ask for. Anyone can wire a demo; interviews probe messy data, chunking, retrieval quality, and drift.
Embeddings & vector search
- Embedding models
- Similarity metrics
- ANN indexes
Vector databases
- Pinecone, Weaviate, Milvus
- pgvector
- Trade-offs across options
Retrieval pipeline
- Chunking strategies
- Metadata filtering
- Hybrid search
- Reranking
Hardening RAG
- Grounding & citations
- Chunk & context limits
- Evaluating retrieval
- Handling corpus drift
Mock this roundTutorials coming soonWhy it matters · Companies are shipping multi-agent and tool-using workflows now. Expect design questions on agent loops, tool calling, memory, and MCP.
Agent fundamentals
- The ReAct loop
- Planning
- Tool calling & function schemas
Frameworks
- LangChain
- LlamaIndex
- LangGraph — when and why
Multi-agent & protocols
- Orchestration patterns
- MCP integration
- Agent-to-agent communication
Memory & state
- Short-term vs long-term memory
- Context management
- Session state
Mock this roundTutorials coming soonWhy it matters · AI loops are not exempt from coding. Big-tech still runs dedicated algorithm rounds, mid-size companies mix one DSA-style screen with a practical exercise, and the AI take-home — 'build an agent', 'ship a RAG feature' — is now a standard round on its own.
Practical coding
- Strings, arrays, hashing
- Real-world logic over puzzles
- Clean, tested Python under a timer
Algorithms when asked
- Common patterns (two pointers, sliding window)
- Trees, graphs & recursion
- Complexity trade-offs out loud
The AI take-home
- Scoping a build-an-agent brief
- Shipping a small RAG feature end to end
- README, evals & trade-off write-up
Working with AI tools
- Pair-programming rounds
- Reasoning about AI-generated output
- Explaining your own code cold
Mock this roundTutorials coming soonWhy it matters · Eval literacy separates real AI engineers from prompt tinkerers. Nearly every senior JD asks for eval pipelines, golden datasets, and LLM-as-judge.
Eval design
- Golden datasets
- Offline vs online eval
- Regression suites
LLM-as-a-judge
- Rubrics
- Pairwise comparison
- Bias & limits
Observability
- Tracing (OpenTelemetry, LangSmith)
- Logging
- Latency & cost dashboards
Quality metrics
- Faithfulness & relevance
- Groundedness
- Hallucination rate
Mock this roundTutorials coming soonWhy it matters · Production AI fails on security, not accuracy. Prompt injection, data leakage, and unsafe output are standard interview and review topics.
Input/output guardrails
- Validation
- Moderation
- Schema enforcement
Prompt-injection defense
- Direct & indirect injection
- Tool-use sandboxing
Data & privacy
- PII handling
- Data leakage
- Access control on retrieval
Responsible AI
- Bias
- Refusal design
- Content policy
- Red-teaming
Mock this roundTutorials coming soonWhy it matters · Most problems are solved by RAG + prompting, but strong candidates can articulate LoRA/QLoRA, cost trade-offs, and when fine-tuning actually wins.
When to fine-tune
- RAG vs prompting vs fine-tuning
- Cost & maintenance trade-offs
- Signals it's actually the right call
Techniques
- LoRA, QLoRA, DoRA
- Instruction tuning
- Datasets
Optimization
- Quantization
- Distillation
- Inference speedups
Evaluation of tuned models
- Before/after eval
- Regression
- Overfitting
Mock this roundTutorials coming soonWhy it matters · The role is distributed-systems engineering with a probabilistic component. Deployment, cost, latency, and monitoring decide the senior rounds.
Serving & scaling
- Deployment
- Autoscaling
- Streaming
- Batching
Cost & latency
- Caching
- Model routing
- Semantic caching
- Budgets
Monitoring in prod
- Drift
- Regressions
- Incident response
- On-call for AI
CI/CD for AI
- Deployment pipelines
- Versioning prompts & models
- Data & prompt pipelines
Mock this roundTutorials coming soonWhy it matters · The AI equivalent of HLD. You'll whiteboard a RAG or agent system: data flow, retrieval, evals, cost, failure modes and trade-offs.
Designing RAG systems
- Ingestion
- Indexing
- Retrieval
- Serving
Designing agentic systems
- Tool boundaries
- Orchestration
- Guardrails
Cross-cutting concerns
- Scalability & cost
- Latency
- Observability
- Safety
Classic prompts
- AI chatbot over private docs
- Doc-QA assistant
- Coding agent
- Recommendation / personalization with LLMs
Mock this roundTutorials coming soonWhy it matters · AI teams move fast and ambiguous. Hiring managers want ownership, judgement under uncertainty, and stories about shipping AI responsibly.
Frameworks
- STAR: Situation, Task, Action, Result
- Quantifying impact with metrics
- Structuring a concise answer
Story bank
- Shipping AI features
- Working through ambiguity
- Failed experiments
- Responsible-AI calls
Values & project
- Company AI principles
- Project deep-dive: architecture & trade-offs
Closing the loop
- Questions to ask the interviewer
- Handling 'tell me about yourself'
- Offer handling & negotiation
Book this mockTutorials coming soon
Don't wait for the offer stage.
Book a 1:1 mock interview with a mentor at any point on this path — after a single stage or a full loop. Get scored, honest feedback on the exact round you're prepping, plus a plan for what to fix next.
Book a mock interviewAI Engineering interview FAQ
- How long does it take to prepare for an AI engineer interview?
- Plan for 21–26 weeks end to end. The Mentoxis AI engineering roadmap orders the work — Python and engineering foundations, how LLMs work, LLM APIs and prompting, RAG, agents, the coding and take-home rounds, evals and observability, and production deployment — so each stage builds on the last instead of scattering your time. Already strong on Python and coding? You can skip ahead and finish considerably faster.
- What skills do you need for an AI engineer job in 2026?
- Prioritise Python and async fundamentals, LLM API mastery, prompt engineering, RAG and vector databases, agents and MCP-based orchestration, evals and observability, LLMOps, and safety and guardrails. RAG shows up in the majority of applied-LLM job listings, so it isn't optional — and most loops still include a coding round. The roadmap lists the exact topics for each area.
- Do I need a machine learning or research background to become an AI engineer?
- No. Applied AI engineering is software engineering with an LLM at the center, not model research — you build on models other teams trained. Most employers weigh shipped project work and production experience above academic credentials. This roadmap keeps theory to what you actually need to explain in an interview — tokens, embeddings, context windows, why models hallucinate — and spends the rest of the time on RAG, agents, evals and production.
- What is RAG and why is it so important for AI interviews?
- Retrieval-Augmented Generation (RAG) grounds an LLM's answers in your own data by retrieving relevant context before generation, instead of relying on what the model memorized during training. It's the most-deployed pattern in production AI, and interviews probe the parts that break in practice — chunking strategy, retrieval quality, and handling corpus drift — not just wiring up a demo.
- Is fine-tuning or RAG more important for AI engineer roles?
- RAG and prompt engineering solve most real problems and are core to nearly every AI-engineering interview. Fine-tuning (LoRA/QLoRA) is a supporting edge — strong candidates know when it actually wins over RAG, not just how to run it. The roadmap treats fine-tuning as a parallel, optional stage for exactly that reason.
- What is the best AI engineering interview roadmap?
- The best roadmap is one ordered so skills compound and mapped to what companies actually test in 2026 — RAG, agents, evals, and production deployment, not just prompting. The Mentoxis AI engineering roadmap does this end-to-end and ties each stage to practice and 1:1 mock interviews with senior engineers.