Interview Prep — AI/ML Engineer

#1. Core ML/NLP Foundations

• Supervised vs Unsupervised Learning
• Classification / Entity Extraction / Sentiment
• NLP libraries: NLTK, spaCy, HuggingFace Transformers
• CV pipelines: classification, detection, OCR, video analytics

#2. LLM Fine-Tuning

• LoRA / QLoRA / PEFT
• RLHF / DPO (instruction tuning)
• Quantization & Distillation
• When to fine-tune vs prompt vs RAG (judgment question)

#3. RAG (Retrieval-Augmented Generation)

• Ingestion → Chunking → Embeddings → Retrieval → Re-ranking
• Hybrid search, query expansion, multi-hop retrieval
• Grounded generation (reducing hallucination at generation time)
• Vector DBs: Pinecone / Weaviate / Qdrant / pgvector / FAISS / Milvus

#4. Knowledge Graphs / Graph RAG

• Neo4j (or equivalent)
• When graph reasoning beats flat vector retrieval

#5. Agentic AI

• Tool calling, planning, memory
• Multi-agent orchestration
• Human-in-the-loop checkpoints & guardrails
• Frameworks: LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen
• Judgment: when NOT to use a framework

#6. Multi-Modal / Multi-Model Systems

• Combining text + image + structured data in one pipeline
• Large-scale distributed multimodal training/inference

#7. Evaluation & Hallucination

• Offline/online eval harnesses
• LLM-as-judge
• Hallucination detection & mitigation
• Task-completion measurement

#8. Serving & Infra (MLOps)

• Inference servers: vLLM, Triton
• Docker, CI/CD, Git, REST APIs, SQL
• Cloud: AWS / GCP / Azure
• K8s, Ray/Spark (distributed compute)
• Kafka/Redis (streaming/caching)

#9. Observability

• LangSmith / Langfuse / MLflow / OpenTelemetry / Arize Phoenix

#10. Governance & Security

• Data protection, access control
• Prompt-injection defenses
• Model risk & compliance monitoring
• Auditability, bias checks

#11. Core ML Stack

• Python (production-quality, tested code)
• PyTorch, Scikit-learn, pandas
• Regulated industry exposure (finance/healthcare/insurance) — bonus