There is no shortcut, but there is a repeatable path: build strong DSA fundamentals (arrays, strings, trees, graphs, DP), revise core CS subjects like OS, DBMS, and computer networks, know your resume projects inside out, and practise explaining your thought process aloud. Most product companies in India test problem-solving over memorised answers, so solve problems on a timer and do mock interviews. If you are figuring out how to prepare for a software engineer interview from scratch, split your week into coding practice, CS fundamentals, and project/behavioural storytelling instead of studying everything randomly.

Freshers are usually grilled on DSA (arrays, strings, linked lists, trees, basic DP), OOPs concepts, DBMS queries and normalisation, OS topics like threads and deadlocks, basic computer networks, and logic puzzles. Expect deep dives into whatever is on your resume — internships, projects, and the tech stack you claim to know. HR rounds cover relocation, salary expectations, and "why this company", so prepare those answers in advance too.

At most Indian product companies it goes: online assessment (coding + MCQs), 2–4 technical rounds focused on DSA and problem-solving, an LLD/HLD or system design round depending on seniority, then a hiring manager round and HR. Service companies lean more on aptitude, CS fundamentals, and communication. Timelines range from a week to a month, so ask the recruiter for the round structure early and prepare round-specific instead of generic.

LeetCode Discuss, Reddit communities, LinkedIn posts, and personal blogs carry detailed round-by-round breakdowns. Search using the company name plus role and year, because processes change frequently. Cross-check at least 2–3 recent posts before deciding what to study, since one person's experience can be an outlier. Reading these also shows you which rounds to rehearse with a friend or a mentor before the real thing.

A neat formal or business-casual outfit — a plain shirt with trousers — is the safe default for interviews in India. Startups are more lenient and smart casuals usually work. Avoid flashy accessories, strong perfumes, and overdressing with a full suit unless the role is client-facing or banking-adjacent. For video interviews, what matters is the top half, good lighting, and a clean background. When in doubt, simply ask HR about the dress code — nobody penalises you for asking.

Google's loop is DSA-heavy: expect multiple coding rounds on data structures, algorithms, and complexity analysis, plus a behavioural round and a system design round for senior roles. Practise medium-to-hard problems while writing clean, well-structured code and narrating your thinking, because interviewers evaluate how you arrive at a solution, not just the final answer. Apply consistently, seek referrals, and give yourself 2–3 months of focused prep alongside a job.

Build fundamentals first: scalability, load balancing, caching, SQL vs NoSQL, sharding, replication, CAP theorem, message queues, and API design. Then study classic designs like a URL shortener or chat app and practise 8–10 problems end-to-end on a whiteboard or paper, ideally with feedback from someone senior. Books and courses help, but the real jump comes from articulating trade-offs aloud under time pressure — simulate that before the actual round.

The classics: design a URL shortener, a WhatsApp-style chat, a Twitter/Instagram feed, an Uber-style ride matching system, a YouTube-style video platform, a rate limiter, a notification system, a parking lot (LLD), and a payment/wallet system. Within each, you will be tested on scale estimation, data modelling, caching, partitioning, and failure handling. Learn 8–10 of these deeply rather than skimming 50, because follow-up questions go deep fast.

Use a fixed framework: (1) clarify functional and non-functional requirements, (2) estimate scale — users, storage, QPS, (3) draw a high-level design covering APIs, database, and services, (4) deep-dive into the hardest component, (5) discuss bottlenecks, trade-offs, and failure handling. Keep asking questions instead of jumping to a solution; interviewers reward structure and trade-off reasoning over a perfect diagram.

The two-volume System Design Interview series by Alex Xu is one of the best starting points — Volume 1 for core building blocks and classic designs, Volume 2 for more advanced, real-world systems. It is enough to build a strong base, but for senior roles pair it with live mock design rounds and engineering blogs, since a book cannot replicate defending your decisions against rapid follow-ups.

If you are starting from zero, yes — it organises design into repeatable patterns like back-of-envelope estimation and standard components, which is exactly what beginners need. If you already have design experience, you may find it basic and will benefit more from Alex Xu's volumes plus mocks. Use it to learn the template, then apply that template to unseen problems, since interviewers deliberately pick non-standard systems.

Paste the job description along with your resume and start with a resume review prompt such as: "Act as a hiring manager for a software engineer role — critique my resume against this JD for impact, relevance, and keywords." Ask it to quantify achievements, remove clichés, and flag anything a recruiter would skip. Then verify every suggestion yourself, because AI can miss context or invent details. For final polish, one round of human review from a mentor or someone who actually hires catches credibility issues AI cannot.

It is a strong first filter, not a final one. AI is good for grammar, formatting, ATS keyword alignment, and trimming fluff. What it usually misses is whether a project actually sounds credible, which achievements to prioritise for a specific role, and how a hiring manager at that company reads your story. The hybrid approach works best — use AI for structure, then get a short expert review on positioning.

Options that actually work: your college placement cell if you are a recent graduate, seniors or peers in similar roles, Reddit communities where volunteers critique resumes, LinkedIn connections who offer feedback, and free AI tools. The catch with free reviews is inconsistent depth — you often get generic checklist feedback. They are fine for formatting fixes, but for repositioning your experience and impact, a focused review from someone who has sat on interview panels is worth the small fee.

A CV review is a structured critique of your CV — checking formatting, keyword relevance for the roles you are targeting, quantified impact in bullet points, and whether anything irrelevant is wasting space. In India, "CV" and "resume" are used interchangeably for tech jobs, so the process is the same. A good review does not just fix typos; it repositions your experience toward what the target role's screening actually looks for.