Comprehensive interview-preparation pack for a Senior AI Engineer / ML Solutions Architect / MLOps & LLMOps role. Originally compiled for an onsite role at Avrioc Technologies (Abu Dhabi, UAE). Generic enough to reuse for any senior AI engineering interview at a product company with a modern LLM stack (vLLM, Kubernetes, Ray, LangGraph, RAG).
18 chapters, ~50,000 words, ~200 printed pages of depth-first content. Every chapter has: concepts, block diagrams, math, code snippets, 20–30 Q&A, and "gotcha" traps.
| # | Chapter | Covers |
|---|---|---|
| 00 | Master Index & JD Alignment | JD analysis, resume→JD mapping, 5 signature stories |
| 01 | Foundations | Neural nets, activations, Word2Vec / GloVe / FastText |
| 02 | Transformer Architecture | Attention, MHA/MQA/GQA/MLA, RoPE, YaRN, ALiBi, FlashAttention |
| 03 | How LLMs Work | Tokenization, pretraining, SFT, RLHF, DPO, KV-cache, inference |
| 04 | Embedding Models | Contrastive learning, InfoNCE, BGE/E5/ColBERT, Matryoshka |
| 05 | LLM Parameter Tuning | Temperature, top-p/k, min-p, penalties, beam, mirostat |
| 06 | Fine-tuning | LoRA, QLoRA, DoRA, PEFT, DPO recipes |
| 07 | RAG | Naive → advanced, hybrid, HyDE, rerank, GraphRAG, RAGAS |
| 08 | Vector Databases | HNSW/IVF/PQ, pgVector/Qdrant/Pinecone/Milvus |
| 09 | Model Optimization | GPTQ/AWQ/GGUF/SmoothQuant/FP8, Wanda, distillation |
| 10 | MLOps & LLMOps | MLflow, feature stores, Langfuse, guardrails, cost cascades |
| 11 | AWS & Azure | SageMaker, Lambda, Bedrock, VPC, Databricks, Azure ML |
| 12 | Kubernetes, Ray, Docker | K8s primitives, KServe, KEDA, Ray Core/Serve/Train/Tune |
| 13 | Frameworks | FastAPI, LangChain, LangGraph, CrewAI, vLLM, Chainlit, Streamlit |
| 14 | Monitoring & Drift | KS/PSI/Wasserstein, Evidently, Datadog, closed-loop retraining |
| 15 | Resume Deep Dive | Every resume bullet with STAR + technical drill-downs |
| 16 | System Design | 4 full designs: RAG, real-time inference, multi-LoRA, streaming agent |
| 17 | Behavioral & HR | 15 behavioral Qs, UAE specifics, compensation negotiation |
| 18 | Cheatsheet | Formulas, numbers, commands, names — morning-of revision |
Beyond the 18 core chapters, interview/ holds domain-, role- and company-specific prep banks (chapters 19+). The most comprehensive recent ones:
| # | Pack | Covers |
|---|---|---|
| 48–53 | Patent & Prior-Art AI — Novelty Checking & Design-Around — 48 Orientation & strategy · 49 Domain primer · 50 System design · 51 Measurement & evaluation · 52 Q&A bank · 53 Explain it simply · runnable demo → | Six-chapter domain pack (~50k words) plus working code on building AI for patent prior-art search, novelty assessment and design-around analysis — written for an ML engineer with no patent-law or chemistry background. 48: how to scope an unfamiliar domain fast — the three discovery questions, cost-asymmetry framing, a traps list, a 30-minute conversation plan. 49: novelty (EPC Art. 54) vs inventive step (Art. 56), the X/Y/A/E/P examiner citation categories, the 18-month publication blackout and Art. 54(3) secret prior art, DOCDB vs INPADOC families, IPC/CPC, selection inventions and the Art. 123(2) added-matter constraint, plus the chemistry layer — Markush structures, SMILES/InChIKey/SMARTS, fingerprints, Tanimoto and its T ≤ min(a,b)/max(a,b) size bound, OPSIN/OCSR/chemical NER — and a free-vs-licensed data map with the embedding/licensing trap. 50: a whiteboard-able reference architecture — nine retrieval channels (BM25, dense, structure, Markush, graph, metadata, citation) fused with RRF, four-granularity chunking, the element × document coverage matrix that turns "one document kills novelty" into an objective function, the design-around/white-space module, an allowed/forbidden table for the LLM, an MCP tool layer, EU AI Act & confidentiality patterns, build-vs-buy, and a phased plan with kill criteria. 51: TF-BIDF and the full Kelly–Papanikolaou–Seru–Taddy construction, Hall's exact Herfindahl bias correction, Uzzi atypicality, Trajtenberg originality/generality, calibration (Platt/isotonic, ECE/Brier), asymmetric-cost review-depth thresholds, PRES, Lincoln–Petersen/Chapman and Chao1 for estimating the recall you cannot observe, TAR stopping rules, and the temporal-leakage traps that make offline numbers lie. 52: ~75 answered questions (RAG, agents/MCP, knowledge graphs, MLOps, architecture judgement, STAR) + 20 to ask back. 53: the plain-language version — the whole system explained without jargon, with block diagrams, a fully worked example, a three-minute narration script, and the obvious questions answered simply. examples/prior_art_demo/: a dependency-free Python implementation you can actually run (python run_demo.py) — three independent retrieval channels with RRF fusion, the EPC date engine, the element × document coverage matrix with quoted evidence spans, Chao1 recall estimation and white-space mapping, over a toy corpus of 12 fictional documents, with 25 tests |
| 47 | Production ML on AWS · 2nd-Round Technical Q&A Bank | Full model answers to a 21-question production/MLOps 2nd-round: model monitoring (4 layers) & metrics (RMSE/MAE, Precision/Recall/F1), drift detection (PSI/KS, data vs concept vs label), safe model updates (shadow→canary→blue-green), rollback across build/prod accounts (traffic-weight flip, no rebuild), IaC/Terraform, load balancing & auto-scaling (ALB/NLB, SageMaker/EC2/Lambda scaling), job scheduling (Airflow/Glue/EventBridge, idempotency & debugging), latency vs throughput (Little's Law, read-heavy trade-off), and orchestrating many models + ETL as one system (DAG control plane + lake/feature-store/registry data plane, data-flow, dependencies, primary keys & join correctness, system dashboards). Each answer in spoken-interview form with gotchas + a morning-of one-liner sheet |
| 46 | Logic20/20 · Offshore Senior MLE (SDG&E Vegetation Management) | Full 2-round notebook for a wildfire vegetation-management MLE seat: Logic20/20 + SDG&E intel (VRI, TreeVision, WiNGS, WMP/OEIS/CPUC, HFTD, grow-in vs fall-in, PSPS), the vegetation-ML domain (LiDAR point clouds, aerial/satellite segmentation, encroachment risk, geospatial stack), resume→role skill-map with honest-gap bridges, a whiteboard-able reference architecture, worked system design, live-coding, per-interviewer game plans, a ~54-question bank, market context, a morning-of cheatsheet, and a do-NOT-state-as-fact honesty section |
| 45 | Google · Staff AI/ML Engineer, YouTube Create | ~50-page L6 pack: JD decode, YouTube Create product & tech intel, the full interview loop, coding bank, CV/video ML, diffusion (Imagen/Veo), ASR & audio, on-device/mobile ML, 6 worked ML system designs with diagrams, Googleyness & leadership, a mock design transcript, and a 40-question rapid-fire bank |
| Day | Focus |
|---|---|
| D-14 → D-12 | Ch 02 (Transformers), 03 (LLMs), 04 (Embeddings) |
| D-11 → D-9 | Ch 05 (Parameters), 06 (Fine-tuning), 07 (RAG) |
| D-8 → D-6 | Ch 09 (Optimization), 10 (MLOps), 12 (K8s/Ray) |
| D-5 → D-4 | Ch 11 (Cloud), 13 (Frameworks), 14 (Monitoring) |
| D-3 | Ch 15 (Resume) — rehearse out loud |
| D-2 | Ch 16 (System design) — 2 mock designs |
| D-1 | Ch 17 (Behavioral), 18 (Cheatsheet) — light review |
| Day of | Re-read Ch 18. Eat. Sleep 8 hrs. |
- Content reflects understanding as of 2026-04. Fast-moving space — verify model names, benchmarks, and prices before quoting in an interview.
- Project narratives in Chapter 15 describe one candidate's specific experience; adapt them to your own resume.
- Not affiliated with Avrioc Technologies or any company mentioned. Use at your own risk.
MIT — see LICENSE.