Pick one pattern at a time — arrays and hashing, two pointers, sliding window, binary search, linked lists, stacks, trees, graphs, then dynamic programming — and solve 2–3 problems on it daily instead of marathoning on weekends. Always code without looking at solutions, maintain a mistake log, and re-solve every failed problem after a week. Work through structured DSA practice interview questions rather than random problems, and once comfortable, start solving under a 30–40 minute timer, since time pressure is where most candidates struggle.

Give yourself 6–8 weeks. Spend the first 3–4 weeks on core data structures and patterns, the next two on graphs and dynamic programming, and the final two on revision and mock interviews. Alongside DSA, revise time and space complexity analysis and practice explaining your approach out loud while coding, because interviewers grade your thought process. If you are targeting a specific company, review its recent interview patterns and difficulty level beforehand.

A mock interview is a simulated version of the real thing where an experienced interviewer gives you unseen problems and evaluates you under actual time pressure. Its purpose is to expose gaps in your problem-solving and communication while there is still time to fix them, get you comfortable thinking aloud, and give you honest feedback that solo practice cannot. Knowing how to give a mock interview properly — video on, shared editor, no pausing — matters as much as doing one.

Most rounds are built around arrays, strings, hashing, two pointers, sliding window, binary search, trees and graphs, with dynamic programming appearing more for experienced roles. Freshers usually get one or two easy-to-medium problems, while candidates with around 2 years of experience face medium-to-hard problems plus follow-ups on optimising time and space complexity. Expect questions like "walk me through your approach" and "can you improve the complexity?" — reasoning is tested as much as the final code.

AI mock interviews are excellent for daily reps — they are available anytime, adapt to your level, and remove the fear of judgement. But they still fall short of a real interviewer who asks unexpected follow-ups, pushes back on your reasoning, creates genuine pressure, and judges code quality the way an actual panel would. The best approach is to use an AI DSA mock interview for regular practice and take a few sessions with a human interviewer before your actual rounds.

Free options — friends, college seniors, peer groups and coding communities — are great for getting started and reducing interview fear. The limitation is that peers usually do not know what interviewers at product companies actually evaluate, so feedback tends to stay superficial. A paid session with a working software engineer gives calibrated feedback, realistic question difficulty and an honest read on your readiness. A practical plan: start with free mocks, then invest in one or two paid ones before important interviews.

Search for DSA mock interview, then filter mentors by current company, years of experience and ratings instead of picking the first profile. Read recent reviews to check whether people actually received detailed feedback, look at what the session covers, and book a slot that leaves you time to prepare. Share your resume and target companies with the mentor in advance so the problems match the level you will actually face.

Build around three pillars: DSA problem-solving, computer science fundamentals (OS, DBMS, OOP, basic networks) and your own projects, which you should be able to explain in depth. Plan 8–12 weeks of structured software engineer interview preparation — daily coding practice, one fundamentals topic per week, and at least 3–5 mock interviews before your target companies' rounds. Keep applying in parallel, because referrals and timing play a bigger role than most candidates admit.

In India, freshers usually face an online assessment with aptitude and 2–3 coding questions, followed by technical rounds heavy on arrays, strings, linked lists and basic SQL. Expect OOP concepts explained with real examples, deep questions on your projects — often the most decisive part of the round — plus a short HR discussion. Practising "explain your project in two minutes" and writing clean code on a shared editor pays off more than memorising answers.

Most product companies follow a similar flow: application or referral, an online coding assessment, two to three technical rounds covering DSA and, for experienced roles, low-level design and system design, then a hiring manager or HR round. Service-based companies often add aptitude tests and group discussions, while startups may compress everything into one long technical round or a take-home assignment. The DSA bar is usually medium LeetCode level, higher for well-paying product roles.

The bar is consistently strong DSA — medium-to-hard problems, especially graphs and dynamic programming — with clean, well-explained code and complexity analysis. For SDE-2 level roles, expect system design as well, along with a behavioural round. Give yourself 4–6 months of consistent practice, focus on approaching unseen problems instead of memorising solutions, and do multiple mock interviews with detailed feedback, since how clearly you communicate your thinking is graded as seriously as the solution itself.

LeetCode Discuss, GeeksforGeeks and company-wise question archives on GitHub are the most used sources, and LinkedIn posts by recent candidates help you track process changes. Prioritise software engineer interview experience posts from the last 3–6 months, because rounds and difficulty levels change often. Study the patterns behind the questions rather than memorising exact answers — companies shuffle problems, but the underlying concepts repeat heavily.

Read it the way a recruiter does — a seven-second skim. Keep it to one page unless you have 8+ years of experience, lead every bullet with a quantified outcome (%, ms, users, cost saved), mirror keywords from the job description so it clears ATS filters, and use a clean single-column format. For tech roles in India, skip photos and personal details like marital status. Finally, re-read purely for typos — small errors signal carelessness to reviewers.

Paste your resume along with the exact job description and ask for specific things: ATS keyword gaps, weak or vague bullets, and rewritten impact-driven versions. A targeted resume review prompt — mentioning the role, experience level and company type — produces far better output than a generic "improve my resume" request. Then verify every suggestion yourself, because AI tends to add generic filler or overstate experience, and experienced recruiters spot that instantly.

A free resume review — AI tools, friends, online communities — catches surface issues like formatting, spelling and missing keywords, and it is the right first step. But if you are getting rejections without interview calls, the problem is usually positioning and impact, which generic feedback rarely fixes. A proper resume review service, ideally from someone working in your target role, gives role-specific, honest feedback on what to cut and how to frame your experience — worth it before you start mass applying.