Top Tech SDE and AI Roles

Derive the time and auxiliary-space complexity of a Java solution containing nested loops, recursion, or standard collections.

Translate a coding prompt into explicit inputs, outputs, constraints, invariants, and edge cases before writing code.

Construct a brute-force solution and identify the bottleneck that an optimized solution must remove.

Solve an array or string lookup problem using a hash map or hash set in Java.

Solve a sorted-array problem using the two-pointer technique in Java.

Solve fixed-size and variable-size window problems using the sliding-window technique in Java.

Implement boundary-safe binary search for exact-match and first-valid-position problems in Java.

Validate and debug a Java solution using targeted normal, boundary, and adversarial test cases.

Answer range-sum queries by constructing and applying a prefix-sum representation.

Merge overlapping intervals after selecting and applying an appropriate sort order.

Reverse a singly linked list iteratively while preserving every node.

Detect a linked-list cycle using fast and slow pointers and justify the space complexity.

Evaluate or validate a nested expression using a stack in Java.

Solve a next-greater-element problem using a monotonic stack.

Generate subsets or permutations using a recursive choose-explore-unchoose template.

Solve a constrained combination problem using backtracking with pruning.

Implement preorder, inorder, and postorder traversal of a binary tree using recursion.

Perform level-order traversal of a binary tree using a queue.

Validate the binary-search-tree ordering invariant using propagated bounds.

Find the lowest common ancestor of two nodes in a binary tree.

Compute a binary-tree path quantity such as maximum depth or diameter using postorder information.

Find the top-k elements of a collection using an appropriately sized heap.

Merge multiple sorted sequences using a heap of current candidates.

Implement trie insertion, exact lookup, and prefix lookup in Java.

Represent a directed or undirected graph with adjacency lists and state its storage complexity.

Count connected regions in a grid using depth-first or breadth-first search.

Find shortest-path distances in an unweighted graph using breadth-first search.

Detect a cycle in a directed graph using traversal state.

Produce a topological ordering of a directed acyclic graph.

Implement union-find with path compression and union by size or rank.

Find shortest paths in a non-negative weighted graph using Dijkstra’s algorithm.

Determine whether an undirected graph is bipartite using two-color traversal.

Define the state, transition, base cases, and evaluation order for a dynamic-programming solution.

Solve a one-dimensional optimization or counting problem using memoization and tabulation.

Solve a grid path problem using two-dimensional dynamic programming.

Solve a subset-sum or 0/1-knapsack variant using dynamic programming.

Solve a sequence or string alignment problem using a two-dimensional recurrence.

Solve an interval-selection problem using a greedy ordering rule.

Distinguish greedy, backtracking, and dynamic-programming approaches for a constrained optimization problem.

Reduce the memory usage of a dynamic-programming solution when only prior states are required.

Explain a coding solution aloud from clarification through complexity analysis without relying on hidden reasoning.

Solve an unseen medium array or string problem in Java within a 35-minute interview window.

Solve an unseen medium linked-list or tree problem in Java within a 40-minute interview window.

Solve an unseen medium graph or dynamic-programming problem in Java within a 45-minute interview window.

Diagnose and repair a flawed Java algorithm from a failing test case and execution trace.

Compare two valid solutions in terms of asymptotic cost, implementation risk, and relevant constraints.

Classify mistakes from a mock coding interview in a reusable error log.

Select and complete a targeted follow-up drill based on evidence from an error log.

Trace a request through DNS resolution, TCP and TLS establishment, a load balancer, an application service, and a database.

Choose HTTP methods, status codes, and idempotency behavior for a backend operation.

Specify a REST API contract with validation, pagination, versioning, and consistent error responses.

Explain how processes, threads, blocking I/O, and asynchronous I/O affect backend throughput and latency.

Identify a race condition in Java code and make the critical operation thread-safe.

Select between locks, concurrent collections, executors, and futures for a stated Java concurrency requirement.

Explain how JVM stack, heap, garbage collection, and resource limits can affect service behavior.

Use latency, throughput, error, CPU, and memory evidence to localize a backend performance bottleneck.

Design a relational schema with keys and constraints for a stated backend use case.

Choose and justify an index based on a SQL query’s filters, joins, and ordering.

Predict common read and write anomalies under standard transaction-isolation levels.

Choose between relational, document, key-value, and wide-column storage for a stated access pattern.

Select a cache-aside, read-through, write-through, or write-behind strategy for a backend workload.

Mitigate stale data, cache stampedes, and hot-key failures in a cached service.

Choose message ordering, delivery, and retry semantics for an asynchronous workflow.

Design an idempotent message consumer that remains correct under duplicate delivery.

Translate functional requirements into responsibilities, interfaces, classes, and relationships.

Refactor a Java design that violates single-responsibility, open-closed, or dependency-inversion principles.

Select a creational, structural, or behavioral design pattern based on a concrete change requirement.

Model an extensible notification or payment-processing service using interfaces and dependency injection.

Design the core objects and interactions for a parking-lot or reservation system.

Implement a thread-safe fixed-window or token-bucket rate limiter in Java.

Write unit tests for a low-level design using fakes or mocks at external boundaries.

Evaluate a low-level design for extensibility, testability, concurrency safety, and unnecessary complexity.

Estimate traffic, storage, bandwidth, and peak capacity from a product scenario.

Choose replication and partitioning strategies for a stated scale and access pattern.

Explain the consistency and availability consequences of a network partition for a specific operation.

Choose load-balancing, autoscaling, and backpressure mechanisms for a variable workload.

Select a unique-ID generation strategy based on ordering, scale, and availability requirements.

Design a scalable URL-shortening service and justify its API, storage, caching, and reliability choices.

Design a real-time chat or notification service and justify its connection, delivery, ordering, and presence mechanisms.

Add failure handling, observability, security boundaries, and disaster-recovery provisions to a system design.

Frame a customer-facing product capability as an ML or AI problem with measurable user, revenue, and model metrics.

Design an online inference service that satisfies stated latency, throughput, availability, and cost targets.

Prevent training-serving skew by specifying consistent feature computation and data contracts.

Define versioning and lineage for datasets, features, model artifacts, prompts, and service releases.

Detect data drift, concept drift, model degradation, and customer-impact regressions using production signals.

Choose shadow, canary, A/B, or phased deployment for a model release and define rollback criteria.

Design a retrieval-and-LLM service that balances answer quality, latency, cost, safety, and graceful degradation.

Decide whether a product requirement should use deterministic software, classical ML, an LLM, or a hybrid approach.

Evaluate a job description for level fit, customer impact, engineering scope, and alignment with backend or production ML goals.

Rewrite resume bullets to foreground scale, reliability, ownership, and measurable product or business impact.

Deliver a two-minute and a ten-minute technical explanation of a production project for different interview stages.

Construct a concise behavioral story demonstrating ownership and delivery under ambiguity.

Construct a concise behavioral story demonstrating conflict resolution or stakeholder influence.

Construct a concise behavioral story demonstrating failure, learning, and subsequent improvement.

Answer follow-up questions about personal contribution, trade-offs, incidents, and outcomes without overstating ownership.

Create a targeted mock-interview and application plan from identified skill gaps, role priorities, and the eight-week deadline.