Senior AI/ML & Data Engineer - Islamabad

A foundations course that builds eleven core data structures from the ground up (arrays, matrices, stacks, queues, linked lists, trees and BSTs, hash tables, hash sets, heaps, graphs and tries), then covers sorting, searching, greedy and divide and conquer. Every chapter has practice problems, flashcards and an assessment. It suits students and career switchers whose foundations came from tutorials and who find pattern courses move too fast.

• Job search
• DSA
• CS fundamentals

A browsable practice library of coding problems that can be filtered by pattern, data structure, difficulty and company, so you can drill exactly the weakness you found this week. Patterns include two pointers, sliding window, hash maps, stacks, backtracking and greedy algorithms, with company filters for Google, Meta, Microsoft, Amazon, Oracle and LinkedIn. Free to browse, with an in-browser editor and solutions.

• Job search
• DSA

A course on AI system design interviews, from classic ML systems such as recommendations, ranking and fraud detection to LLM serving costs, RAG, generative AI products and agentic systems, with a repeatable framework and full case studies. It takes about 20 hours across 9 chapters. It suits engineers who can write code and know what a model, an API and a database are, and are facing design rounds that now include AI.

• AI
• System design
• Job search

A beginner course that gives software engineers and students a clear, practical grounding in modern AI: core concepts, training a simple model, neural networks and deep learning, search algorithms, reinforcement learning, agentic AI, modern tools and prompting, and the ethics and hype around it all. It takes about five hours across 14 chapters with playgrounds and assessments, and needs no prior AI background.

• AI
• Machine learning

Accessible write-ups of Google Research work across machine learning, algorithms, systems, health, climate and other sciences, usually linking to the underlying paper. New posts appear most weeks. It is closer to research than production engineering, so it suits students and engineers who want to see where techniques in forecasting, efficient training, retrieval, tool use and generative AI come from before they reach products.

• System design
• Machine learning
• AI

A quick-revision companion to the DSA roadmap in nine chapters: complexity, arrays and hashing, linked lists, stacks and queues, recursion, sorting and searching, trees and heaps, graphs, coding patterns, dynamic programming and timed practice habits. Costs are stated for every structure and algorithm, in Python, Java, C++ or JavaScript editions. Use it after each roadmap stage and before a contest or coding round.

• Programming
• DSA
• Web