Senior Software Engineer - Data Engineering & AI

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

The second volume of Grokking the System Design Interview, aimed at senior and staff (L5/L6) design rounds. It works through harder case studies such as a payment system, flash sale, notification service, unique ID generator, Gmail, Reddit, Google Calendar and a code judging system, then goes beneath them with patterns like quorum, write-ahead logs and vector clocks and deep dives into Dynamo, Cassandra, Kafka, Chubby, HDFS, GFS and BigTable.

• System design
• Backend
• Job search

A quick-revision book on MLflow in Python for engineers who train, compare, ship and monitor models: experiment tracking, autologging and search, the tracking server and stores, models, flavours and signatures, the Model Registry with aliases, serving and deployment, evaluation, GenAI tracing and prompts, and MLOps practice. It notes where MLflow 3 changed things. Use it when setting up tracking, before shipping or promoting a model, and alongside the MLOps and LLMOps roadmap.

• AI
• Machine learning
• Programming
• DevOps

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