Video summary

6 CGPA to Microsoft 90 LPA Job, Copy his Exact Roadmap

Main summary

Key takeaways

Educational

Main ideas, concepts, and lessons

  • Low CGPA doesn’t automatically block hiring if you can demonstrate stronger signals—skills, competitions, projects, internships, referrals—and perform well in interviews.
  • Don’t center your resume on your “weakness.” Naman describes omitting CGPA from resumes and instead leading with strengths (especially competitive programming achievements).
  • AI changes the game (especially for learning + building).
    • Instead of being blocked by missing knowledge (front-end/back-end, etc.), use AI to start and iterate quickly.
    • Emerging career focus: AI Engineering—using models and orchestration tools rather than only building models.

Methodology / instruction-style guidance (from the video)

1) How to handle placements/interviews when CGPA is low

During college

  • Accept that some on-campus placement shortlisting may fail due to CGPA.
  • Build compensating evidence:
    • Competitive programming consistently (e.g., become a higher-ranked candidate/masters-level).
    • Include top coding competition ranks on your resume (e.g., global rank, “first point”).
    • Do not include CGPA in your resume if you believe it will hurt selection and your skills can carry you.

For internships

  • CGPA can still affect certain internship screens.
    • Example implied: Naman’s Adobe internship was rejected after someone asked about CGPA.
  • Strategy implied: rely on strong performance in the process and ensure paper screening isn’t derailing you.

2) Resume strategy taught by the speaker’s experience

  • Put your highest strengths first:
    • Competitive programming results (rank/global rank) instead of CGPA.
  • Only mention minimal academic info:
    • College + graduation year + branch (omit CGPA in this case).
  • If you later apply to higher studies (e.g., masters), CGPA becomes more important, so the “omit CGPA” approach may not generalize.

3) Building in the next 90 days (explicit roadmap)

First 60 days (foundation)

  • Learn key CS fundamentals:
    • DSA (Data Structures and Algorithms)
    • OS, DBMS (operating systems, databases) and other core subjects
    • System design basics
  • Use the time to build theoretical competence and practice habits.

Last 30 days (build)

  • Build a project that demonstrates you can apply fundamentals.
  • The project does not have to be domain-specific.
  • Emphasis: start with your existing strengths and enhance later.
  • Projects can be simple at first:
    • Examples mentioned: tic-tac-toe, chess, dictionary (console/terminal based).
    • With AI, upgrade by adding UI later.

4) How to use AI to avoid “bottlenecking” yourself

  • If you don’t know front-end/back-end/design:
    • Start building anyway with AI assistance.
  • Practice a feedback loop:
    • Build something → get output → refine → publish/resume it.
  • Even with little/no users:
    • Small adoption counts as credibility (e.g., “some people are using it”).

5) Freelancing approach (how Naman got first gigs)

  • Avoid saturated platforms if possible:
    • Use “cold messaging” and be in the right place at the right time.
  • Use confidence + rapid learning:
    • If a client needs something you haven’t done (e.g., migrating AWS → Azure):
      • Understand the problem statement
      • Spend a short, focused period learning via resources (e.g., YouTube)
      • Show confidence in the meeting
  • Deliver, then use the success for more opportunities:
    • The first freelance becomes a confidence booster and leads to further gigs.

6) Interview preparation constraint workaround (mock interviews)

  • Mock interviews may be effective but often hard to access (cost + availability).
  • Solution introduced: HigherGroundAI / Hiram.ai
    • An AI agent conducts mock interviews for DSA:
      • Gives a problem
      • Requires you to solve while speaking/explaining
      • Opens a code editor
      • Asks follow-up questions (complexity, alternative approaches)
      • Produces detailed scoring + improvement report
  • Core evaluation focus:
    • How you explain and structure the approach (brute force → optimization)
    • Communication clarity/coherence
    • Not only whether you know the solution

Speakers / sources featured (as identified)

Speakers

  • Naman (main interviewee)
    • Software engineer at Microsoft
    • Previously Adobe
    • Before that: a short stint at Dsha/Dish (spelling ambiguous in subtitles)
    • References graduating in 2020 with a 6 CGPA
  • Host / interviewer
    • Starts with “Hi everyone. So here is Naman…”
    • Asks questions; name not given

Sources / platforms mentioned

  • Adobe (internship)
  • Microsoft (current employer)
  • AWS and Azure (freelance migration example)
  • LinkedIn (referrals implied)
  • Codeforces (competitive programming; “candidate master” mentioned)
  • LeetCode (DSA practice / solution cramming reference)
  • ChatGPT (discussing why a solution fails)
  • LangChain and AutoGen (AI orchestration platforms)
  • YouTube (learning resources for building/starting)
  • System design books/resources:
    • “Grokking the System Design Interview” (exact titles mentioned are partially garbled)
    • “Designing Data-Intensive Applications” (subtitle text garbled in provided text)
    • Strive / NeetCode (practice resources; subtitle text partially unclear)

Products / services promoted

  • hiram.ai / “Highram.ai” (mock interview AI product; promoted by the interviewee/speaker)

Original video