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Comprehensive Algorithms Portfolio for Senior/Staff-Level Interviews

This portfolio outlines the algorithms that a strong candidate should master for interviews in DevOps, AI, LLM, Data Science, ML, and AI Training. It combines core CS fundamentals, systems/DevOps algorithms, ML/AI algorithms, and LLM/AI training techniques, along with advanced enhancements to stand out in senior/staff-level interviews.

1. Core Computer Science Algorithms

Must-Haves: - Sorting: QuickSort, MergeSort, HeapSort - Searching: Binary Search, Hashing - Graphs: BFS, DFS, Dijkstra, Bellman-Ford, Floyd-Warshall, Topological Sort, SCC - Dynamic Programming: Knapsack, LCS, LIS, Coin Change, Edit Distance - Data Structures: Trie, Segment Tree, Fenwick Tree, Union-Find, Heaps, LRU/LFU Cache

Enhancements: - Max Flow / Min Cut (Ford-Fulkerson, Edmonds-Karp, Dinic’s Algorithm) - String Matching: KMP, Rabin-Karp, Aho-Corasick - Approximate Counting: Count-Min Sketch, HyperLogLog - Advanced Scheduling: Bin Packing, Interval Partitioning

2. Systems / DevOps Algorithms

Must-Haves: - Rate Limiting: Token Bucket, Leaky Bucket - Consistent Hashing (load balancing, distributed caches) - Leader Election (Bully, Raft basics) - Gossip Protocols (eventual consistency, failure detection) - Consensus: Raft, Paxos

Enhancements: - Raft Log Replication (deep dive into distributed logs) - Byzantine Fault Tolerance (PBFT) - Vector Clocks & Lamport Timestamps - Reservoir Sampling (streaming data)

3. Machine Learning / AI Algorithms

Must-Haves: - Gradient Descent (Batch, SGD, Mini-batch) - Regression: Linear, Logistic - Clustering: K-Means - Naive Bayes - PCA (Dimensionality Reduction) - TF-IDF (Feature Extraction) - Backpropagation (Neural Networks)

Enhancements: - Random Forests, Gradient Boosting (Ensemble Methods) - Graph Neural Networks (GCNs, GATs) - Reinforcement Learning: Q-Learning, Policy Gradients - Probabilistic Models: Gibbs Sampling, Variational Inference

4. LLM / AI Training Algorithms

Must-Haves: - Attention Mechanism (Scaled Dot-Product Attention) - Beam Search (sequence decoding) - Sampling: Top-k, Top-p (Nucleus Sampling) - Distributed Training: Data Parallelism, Model Parallelism - Checkpointing & Sharding

Enhancements: - Multi-Head Attention (Transformer core) - FlashAttention (efficient attention computation) - ZeRO Optimizer (DeepSpeed memory optimization) - LoRA / Parameter-Efficient Fine-Tuning - Mixture of Experts (MoE)

5. Mapping to Interview Scenarios

  • DevOps / Systems Design: Consistent Hashing, Raft, Gossip, Rate Limiting, PBFT
  • Data Science / ML: Regression, K-Means, PCA, Random Forests, GCNs
  • AI / LLM Training: Attention, Beam Search, FlashAttention, ZeRO Optimizer
  • General CS / Problem Solving: Graph algorithms, DP, Max Flow, String Matching

6. Why This Portfolio Stands Out

  • Covers fundamentals (sorting, searching, DP, graphs).
  • Includes systems-level algorithms critical for distributed systems and DevOps.
  • Demonstrates ML/AI depth with both classical and modern algorithms.
  • Highlights cutting-edge LLM training optimizations (FlashAttention, ZeRO).
  • Balances breadth and depth, showing readiness for senior/staff-level interviews.