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