Keep it to one page (two only if you have 5+ years of experience), and make every bullet start with an action verb and end with a measurable outcome — "reduced API response time by 40%" instead of "worked on backend services." Mirror the keywords from the job description naturally so the resume passes ATS filters, put your strongest experience or projects at the top, and remove filler lines like "hardworking team player." Before applying, get it reviewed by someone who has cleared product-company interviews, since small wording and formatting mistakes are the most common rejection reason.

A strong software engineer resume is a clean, single-column document with these sections in order: contact details with LinkedIn and GitHub links, a short skills section (languages, frameworks, tools), work experience or projects written as impact-focused bullets, and education. Use a simple 10–11pt font with plenty of white space, and skip photos, tables, icons, date of birth, and "declaration" sections — recruiters and parsing software at product companies prefer plain, scannable pages they can read in 30 seconds.

As a fresher, lead with two or three solid projects (tech stack, live link, and the problem each one solved), then internships, skills, competitive programming profiles such as LeetCode or Codeforces ratings, relevant coursework, and education with your CGPA if it is strong. Recruiters hiring freshers want proof you can actually build things, so one deeply explained project beats a long list of technologies. Keep it strictly to one page and drop school-level achievements, hobby lines, and objective statements.

Start with the free templates in Google Docs or Word — they are ATS-safe and easy to edit — or use a well-known LaTeX template if you want something more polished. Many students build their software engineer resume on Overleaf because the LaTeX templates there are clean, modelled on big-tech resumes, and easy to version and update over time. Whichever template you pick, avoid heavy multi-column Canva-style designs, since parsing software often scrambles them and your resume may reach the recruiter as a mess.

A mock interview is a practice session that simulates the real thing — same format, same type of questions, same time pressure — followed by honest feedback on your answers, communication, and approach. For software roles it usually means solving a DSA problem or discussing a design while explaining your thought process out loud, just as you would in the actual round. The feedback is the entire point: a mock without detailed, honest feedback is just extra practice, not preparation.

Expect a mix of one or two live DSA problems (arrays, strings, trees, graphs, dynamic programming), deep-dive questions on your resume projects ("why did you choose this design?"), CS fundamentals like OOP, OS, DBMS and networks — which Indian product companies still ask — plus system design for experienced roles and behavioral questions such as handling a conflict or a failure. Practising each category separately makes the real interview feel familiar instead of frightening.

Paste the job description and your resume, ask ChatGPT to act as a strict interviewer for that exact role, then answer one question at a time out loud and have it grade you against a rubric, including follow-up questions the way a real interviewer would. A mock interview with AI is genuinely useful for reps — question variety, thinking aloud, and spotting weak answers — but it cannot recreate the pressure, unpredictability, or judgment of a human interviewer, so treat it as warm-up and do at least a few mocks with an experienced engineer before the real day.

Free options — practising with friends or classmates, peer-mock communities, and AI tools — are excellent for building fluency and reducing nervousness in the early stage. The limitation is feedback quality: free partners usually cannot tell you whether your approach would be judged as clean or mediocre by an actual FAANG interviewer. A practical split is to use free practice for volume and reserve a small number of sessions with someone from a product company for calibrated feedback.

For most freshers, yes — the biggest rejection reason in campus interviews is rarely weak DSA; it is freezing while explaining your thinking, fumbling project questions, or rambling in the HR round. A mock interview for freshers should cover a live coding problem, resume grilling, and common HR questions, and even three to five sessions before placement season noticeably improves composure. If your college does not run formal mocks, set them up with seniors, batchmates, or mentors and ask for blunt feedback.

Stop chasing problem counts and practise by pattern instead — two pointers, sliding window, binary search, trees, graphs, DP — so you learn to recognise which technique a new problem needs. Solve under a timer, explain your solution out loud before coding, write actual running code rather than pseudocode, and revisit every problem you failed a few days later until you can solve it cold. This is far closer to real interview conditions than silently reading solutions.

A workable FAANG interview preparation roadmap runs 3–6 months: spend the first 4–6 weeks building DSA fundamentals (arrays, hashing, linked lists, trees, graphs, recursion), then 8–12 weeks of pattern-based problem practice with timed sessions, while preparing STAR-format behavioral stories from your own projects in parallel. Experienced candidates should add high-level and low-level system design as a dedicated phase. Throughout, keep refining your resume, prioritise referrals over cold applications, and finish with full-length mock interviews.

DSA still matters, but FAANG interview preparation for experienced engineers adds heavyweight system design rounds — HLD covering scaling, caching, and databases, plus LLD/OOD at many companies — along with deeper behavioral rounds that judge ownership and impact using your real work. Interviewers will also drill into the architecture and trade-offs of your past projects, so your resume gets scrutinised more strictly. Freshers are tested on potential; you are tested on judgment, so prepare to justify every decision you have made in production.

Start with DSA, because it is the common filter in almost every Indian product-company loop regardless of your college tier or current employer. If you are in a service-based company or a tier-2/3 college, plan for off-campus routes: a referral-ready one-page resume, a well-maintained LinkedIn and GitHub, and targeting product companies' India offices in Bengaluru, Hyderabad, Pune, and Gurugram during high-hiring quarters. Give yourself a 4–6 month window and validate readiness with mocks before burning applications.

If you are self-disciplined, free resources cover nearly all the content — curated problem lists, system design primers, and full video playlists — so a paid FAANG interview preparation course mainly buys you structure, deadlines, and accountability. What free material cannot give you is personalised feedback on your resume, your communication, and how you actually perform under questioning. If you keep postponing prep or keep getting rejected without knowing why, structured guidance or 1:1 mentorship usually fixes that faster than another course.

Start with Cracking the Coding Interview — it explains how interviewers evaluate answers and covers core DSA with worked examples — then move to Elements of Programming Interviews for tougher, interview-grade practice. Experienced engineers should add a system design book like Designing Data-Intensive Applications alongside. Remember that a FAANG interview preparation book builds understanding, but selection comes from solving problems live under pressure, so pair your reading with consistent timed coding practice and mocks.