From a Village to Lowe’s: How Pranav Cracked a 11.5 LPA Software Engineer Job

Main ideas, concepts, and lessons

Personal and career background

• Pranav grew up in a village in Karnal and moved to a city only after deciding to prepare for IITJ (staying alone via a PG).
• He didn’t reach IIT, but ended up in Bennett University (Greater Noida, UP), largely due to a ~50% scholarship because of financial constraints.
• Academically, he leaned toward mathematics/physics and performed strongly (near 9 CGPA/SGPA).
• However, coding/development initially felt harder because he hadn’t coded in 11th/12th.

Early learning struggles and the role of “wake-up calls”

• In his first couple of years in college, he focused on the curriculum (math/physics) and used AI-assisted approaches for assignments.
  • He avoided blindly copying; after submission, he would review and ensure he understood the work.
• In internships, he noticed clear gaps:
  • He could solve easy/medium problems but struggled with harder ones.
  • In a Gurugram cloud-related internship, he didn’t know basic Linux/Unix (he was on Windows and lacked command knowledge).
  • He also lacked the habit of reading documentation.

Transition from client-side to systems/backend thinking

• After learning frontend documentation (React/Next docs), he realized he was more interested in:
  • Backend/system work
  • Distributed systems
• He chose to resign without an offer to pivot toward this direction—prioritizing long-term fit over short-term certainty.

Strategic investment in training (ACU job)

• Financially, he treated training as an investment:
  • He saved from internships/apprenticeships and used that money to buy the ACU job course.
• He selected fasttrack vs full-time based on his situation:
  • He already had systems/DSA knowledge from internships, so fasttrack fit his time constraints.
  • He later advised others to prefer full-time mentorship if they have less prior knowledge.

Course-to-industry alignment and tool/tech switching

• He noticed a mismatch:
  • Rakuten internship was heavily Java/legacy + large frameworks.
  • His ACU track (as stated) was oriented around MERN/MERN-stack.
• He asked his mentor (Abhishek) to switch the course to the Java track.
• The course helped him connect fundamentals (DSA/system design concepts) with real systems work.

Real-world system design learning

• He emphasized that system design isn’t learned from courses—it’s learned through internships.
• He described a “bottom-up” shift in how he built and learned projects:
  • from clones/tutorial products
  • toward understanding how things work internally (networking/API/building).
• He highlighted core CS fundamentals discovered through industry:
  • Computer Networks
  • Operating Systems
  • DBMS
  • (He also mentioned TBMS/“TSA” as core subjects from memory.)

Project-building mindset (1.0 → 2.0)

• Pre-Rakuten mindset (Project 1.0):
  • Watch YouTube videos → build the same project → publish (repeatable clones).
  • Concern: many people did the same clone projects, so differentiation was limited.
• Post-Rakuten mindset (Project 2.0):
  • Treat projects as products/learning systems built from first principles.
  • Avoid aiming to “make the next ChatGPT.”
  • Build foundational tools such as:
    • small APIs (learning backend beyond “calling APIs in Postman”)
    • small systems involving networking and data flow
    • a small AI review system as an example of simpler, learnable tooling

English fluency improvement (practical approach + mentorship)

• He improved English via:
  • A school teacher who supported him and corrected his weakness (he used to “process in Hindi then speak in English”).
  • Finding mentors across school/company to identify “blind spots.”
• Methods he recommends:
  • Build from basics, not “role-model mimicry.”
  • Consume media in the target language:
    • watch English series
    • avoid copying inaccurate English from unrelated role models
  • Read novels/books consistently:
    • started with Chetan Bhagat
    • later switched to better books after realizing earlier reading was “primitive”
  • Vocabulary rule:
    • you can’t understand a language without sufficient word knowledge
  • Maintain a reading routine (e.g., reading time on Sundays)

Placement/job hunt approach (ACU job + multiple constraints)

• He researched placement outcomes and company legitimacy rather than blindly applying.
• He noted that landing an interview can be harder than landing a job in the market.
• His priority criteria:
  • Full-time + company fit (targeting companies above certain levels)
  • Selectivity reduced distractions; he applied only to a small set of companies.
• He actively did:
  • DSA brushing
  • Spring Boot
  • system design learning via internship experience
• He emphasized discipline and integrity:
  • “Cheating doesn’t work” in AI/modern interviews, especially for deep framework knowledge.
  • He practiced honestly and expected evaluation accordingly.

Interview preparation: what worked

• OA/interview filtering
  • Core fundamentals were essential:
    • DSA (and DBMS as “bare minimum,” plus OS/CN as potential strengths)
  • Easy/medium LeetCode-level comfort is often enough to clear DSA-style screening.
• Technical interviews
  • He used a progression:
    • start with brute force
    • explain it
    • then move toward improved solutions (including DP/optimization paths)
  • He emphasized thinking out loud and communicating reasoning.
• Modern AI usage interviews
  • Companies care about responsible AI usage:
    • not blind “VIP coding”
    • using AI to reduce repetitive work and invest time into innovation/ownership
• HR interviews
  • Focus was manager-like and cross-functional:
    • communication and client/team handling
    • verifying depth of internship work (HR asked whether experience was real or just CV-filling)
    • proving you can justify AI budget impact (not create liability)

Career/offer decisions and timing

• He had an apprenticeship opportunity at Autodesk but still pursued the full-time path.
• Selection timing had contingencies (e.g., test rescheduling while traveling).
• He noted that Rakuten conversion to PO wasn’t guaranteed due to business/team budget constraints—not only talent.

Key philosophy he repeats

• Stay curious and explore “rabbit holes,” but:
  • don’t chase outcomes immediately
  • focus on clarity and ownership of fundamentals
• Own what you know and what you don’t know—this differentiates people.
• Engineering is iterative: systems may become “absurd” later, so you must keep learning and adapting.

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Detailed bullet-point list of methodologies / instructions mentioned

A) Mentorship and learning mindset

• Find strong mentors in every environment:
  • school
  • company
  • courses/mentoring programs
• Treat “blind spots” as normal:
  • someone exists who can clarify them
• Build opinions and stick to them, but stay malleable:
  • strong convictions with room to revise

B) Building English fluency

• Start by fixing the process of speaking:
  • move from translating internally (Hindi → English) toward direct English expression (with teacher help)
• Improve English by building basics rather than copying unreliable models:
  • don’t rely on mimicry of flawed role-model speech
• Consume media in the target language:
  • watch English series
• Read consistently:
  • novels/books build vocabulary and comprehension
  • if earlier reading was “primitive,” upgrade material
• Make reading habitual:
  • schedule reading time (e.g., weekly routine like Sundays)
• Vocabulary rule:
  • you can’t understand a language without enough words

C) Coding/projects approach (clone → foundational systems)

• Don’t stop at tutorial clones:
  • clones are a starting point, but they don’t differentiate
• After industry exposure, shift to learning from first principles:
  • understand how APIs work beyond calling them in tools
  • understand networking and how data travels
  • build smaller foundational projects progressively
• Example project directions:
  • small API services
  • networking-aware components
  • simple AI review systems (manageable tooling)

D) Preparation strategy for placements/interviews

• Course selection:
  • if already strong and time-limited, fasttrack can work
  • if you’re not well-built yet, prefer full-time mentorship
• Placement strategy:
  • research companies and outcomes transparency (don’t assume)
  • apply selectively based on fit rather than applying everywhere
  • aim to “land interviews,” since that’s often harder than getting jobs
• Technical preparation:
  • DSA fundamentals are primary for OA/interview filtering
  • DBMS is recommended as a minimum baseline
  • strengthen (or at least understand) OS and CN for deeper system understanding
• During interviews:
  • use a structured progression:
    • brute force → discuss improvements → aim for optimized (often DP/optimization)
  • explain your reasoning clearly (“think out loud”)
• AI tool usage in interviews:
  • use AI responsibly:
    • reduce repetitive work
    • don’t rely on blind “enter key” solutions
    • show ownership and deeper understanding
• Integrity:
  • don’t cheat; if you don’t know frameworks, it will show

E) Work-study cycle while employed

• When working full-time/internships and preparing:
  • schedule learning after work (rest briefly to handle fatigue, then study)
  • read docs actively; don’t only watch videos
  • treat frameworks as “wrappers” over fundamentals

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Speakers / sources featured (as mentioned in subtitles)

• Pranav (main speaker; job candidate)
• Host/interviewer: “Abhishek”
  • appears as his mentor and as the person guiding him during the ACU job course (shown in host dialogues)
• Another interviewer/mentor person in ACU job
  • a “young fellow/company people” who gave feedback after his interviews (name not clearly specified)
• HR interviewer at Lowe’s (name not provided)
• Rakuten (internship/learning experience)
• Autodesk (apprenticeship/contract source)
• Lowe’s (company he is about to join as a software engineer)
• ACU job / AIO job (training/placement program referenced throughout)
• Bennett University
• Think/quote sources referenced
  • Steve Jobs (connecting dots)
  • Donald Trump (example used against English mimicry)
  • Chetan Bhagat (early reading author)
  • “Steve Jobs” again via connecting-dots idea
• Racha Mahita (school teacher credited with helping his English)