Interview – AI/ML Technology Stack


AI/ML Full Stack Tech Stack

(1) Foundations

  1. Programming
  2. Math 
  3. Statistics

(2) Data Engineering & Data Infrastructure

  1. Data Engineering
  2. Data Storage
  3. Data Pipeline
  4. Data Visualization
  5. Big Data & Distributed Computing
  6. Caching & High-Speed In-Memory Stores
  7. Data Streaming & Event-Driven Architecture

(3) Core AI/ML Model

  1. Machine Learning
  2. Time Series Analysis & Forecasting
  3. Reinforcement Learning
  4. Recommendation Systems
  5. Deep Learning
  6. Natural Language Processing
  7. Computer Vision

(4) Generative AI

  1. Large Language Models
  2. Prompt Engineering
  3. Vector Databases & Embeddings
  4. Knowledge Graphs
  5. Fine Tuning & Customization
  6. Enterprise RAG
  7. Model Evaluation & Quality
  8. Deployment & Serving
  9. Safety & Guardrails
  10. Generative AI Beyond Text

(4) Agentic AI

  1. Agent Architecture & Reasoning
  2. Multi-Agent Systems & Orchestration
  3. Tool Use & Function Calling
  4. Memory Systems
  5. Frameworks & Development
  6. Enterprise Integration
  7. Governance & Safety
  8. Evaluation & Observability
  9. Deployment Patterns

(5) Software & Systems Engineering

  1. Software Engineering
  2. Front End Technology
  3. System Design & Distributed Systems Architecture
  4. Model Serving & Inference Optimization

(6) Cloud & Platform Engineering

  1. Cloud Expertise
  2. DevOps
  3. MLOps

(7) Trust, Quality & Operations

  1. Model Evaluation & Experimentation
  2. Observability & Monitoring
  3. Responsible AI & Security
  4. Model Interpretability & Explainability (XAI)
  5. Data Governance & Quality

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