Podcast
Ready Set Do
Hosted by Naman Pandey · EN
Learn relatably from high-agency individuals, from all walks of life — currently just a few steps ahead in your journey of choice.
The only podcast where you learn from artists, sages, techies and children - and everyone in between.
What makes the stories on Ready Set Do podcast real, relatable, and actually useful is that they aren't selling you lottery tickets they already won with.
Instead, we show you the first few steps they took- so you can find your own way forward. No spoon-feeding, ever.
New episodes every Wednesday.
31episodes
Episodes
Newest firstAll episodes
How to Make $80K on Upwork (From a Top 3% Freelancer) - w/ Anas
1w ago00:45:10Tap to summarizeTwo years ago, Anas lost his full-time UK job. He went back to Upwork — the platform he'd already used to make over $80K while studying — and within months landed an 18-month Swedish contract that generated over $5M in client revenue.His unfair advantage? A 60-second intro video that gets him hired in 24 hours.In this episode of Ready Set Do, I sit with Anas, a Top Rated Plus Upwork freelancer (roughly the top 3% on the platform), to walk through his exact playbook for freelancing on Upwork in 2026.We cover how to start freelancing on Upwork with zero reviews, how to write Upwork proposals that actually get opened, how to price freelance work across countries without underselling yourself, and how to escape the "Upwork is saturated" trap that keeps most beginners stuck.Anas is a Moroccan-born industrial engineer who pivoted into data science, freelanced his way through grad school in the UK, and rebuilt his income on Upwork after a layoff. So he's lived both sides — the broke student grinding for first reviews, and the senior freelancer competing against AI and offshore pricing.If you're trying to figure out how to get clients on Upwork, build a freelance data science career, or future-proof yourself against AI eating junior work — this is the full breakdown.A few things we get into:→ The Upwork proposal mistake 90% of beginners make (and the niche-down fix)→ Why milestones beat hourly contracts on bigger projects→ How to filter bad clients before you ever get on a call→ The LinkedIn + YouTube combo that pre-sells you before the proposal lands→ Where Anas thinks "Chief AI Operator" roles are headed nextWhether you're sending your first Upwork proposal this week or you've been freelancing for years and feel the AI pressure closing in, Anas gives you the tactical playbook and the bigger story behind it.(Quick heads-up: I learned more about freelance pricing in 25 minutes of this conversation than in any Upwork course I've seen.)Subscribe to Ready Set Do for more honest career conversations with people who've actually done it — international students, immigrant founders, freelancers, PMs, and engineers building unconventional paths into tech.🔗 ABOUT ANASLinkedIn → https://www.linkedin.com/in/riadanas/YouTube → https://www.youtube.com/@UC2L6md1QyYAMRAoar_9ZFawReady Set Do podcast: readysetdopodcast.comChapters:00:00 The Biggest Misconception About Freelancing01:55 From Industrial Engineering to Data Science03:15 Saturation, AI Tools, and the Changing Job Market05:00 Starting on Upwork Before Feeling Fully Ready06:42 Niching Down and Writing Targeted Proposals09:47 Getting First Reviews and Building Social Proof11:01 Filtering Clients and Setting Clear Project Boundaries14:32 Milestones, Hourly Work, and Project Structure19:19 Pricing, Undercharging, and Negotiating Rates24:02 Re-Entering Upwork and Landing a Larger Client28:47 Building Trust with LinkedIn, YouTube, and Video Proof35:28 AI's Impact on Freelance Data Work and Future Roles38:13 AI Agents, Chief AI Operators, and Guardrails
Transcribe →How To Get Hired For Agentic AI Big Tech Roles in 2026 (Amazon Sr Data Scientist POV) - w/ Surya
1w ago00:45:36Tap to summarizeHere's the uncomfortable thing nobody at career services will tell you: "I use Claude every day" stopped being a resume line about six months ago. The people hiring at Amazon, Google, and the frontier AI labs already assume you do. So what actually gets you hired in 2026?I sat down with Surya Kari, a Senior Generative AI Data Scientist at Amazon, to find out.Surya works on Amazon's white-glove GenAI team. His days are spent shipping with Fortune 500 customers and the frontier model labs you read about on Twitter. He's in the room when hiring decisions get made. And his honest read on early-career AI hiring is way more specific than the LinkedIn-thinkpiece version.We start with the thing I keep seeing destroy promising careers: using Claude (or any AI tool) like glorified autocomplete. Surya calls this the fastest way to plateau in your twenties. The fix isn't more tools — it's depth. The kind of moat that doesn't melt the next time a frontier model ships.Then we get into agents. Surya doesn't think agentic AI is actually production-ready yet (and yes, he works at Amazon, so he's seen the receipts). We talk about what's still broken, what the hype is missing, and what "agents" will probably mean by the time you graduate.His own story is pretty wild too. He was running a Canadian startup competing against Amazon Go before he ended up running GenAI deals at Amazon itself. The arc from analyst to startup founder to senior AI scientist is full of stuff that won't show up on a tidy career-advice carousel.The conversation I'm proudest of comes near the end. Surya grew up in India, studied in Canada, works in the US — and he's watched how each region is building AI from the inside. The contrast between how the East and the West think about AI right now is sharper than I expected. His take on India treating AI as public infrastructure is the kind of thing you don't hear in the usual big tech hiring discourse.If you're a CS student or new grad trying to figure out where to bet your career in the generative AI era, this one's for you. Surya tells you exactly what he'd do today if he were starting over.Subscribe for weekly conversations with the people figuring out what work actually looks like in the AI era.Connect with Surya:LinkedIn: https://www.linkedin.com/in/suryakari/⏱ Chapters:00:00 A Day in the Life of a Generative AI Data Scientist01:17 Understanding Customer Personas in Generative AI04:17 Upskilling in a Rapidly Evolving Field06:09 The Contrast of Experience in AI Tools09:33 Navigating Production Code and Testing12:15 The Hype Around AI Agents16:28 The Future of AI Agents and Their Limitations20:56 Surya's Journey: From Analyst to AI Expert23:48 Innovations in Retail Technology24:09 Transitioning to Edge Computing and AI26:36 Upskilling in Data Science and AI31:39 Cultural Differences in AI Development36:10 AI as Public Infrastructure in India
Transcribe →How to Build a Global Health Career (Public Health Master’s, Oxford & WHO Africa) - w/ Dyuti
2w ago01:11:24Tap to summarizeHow do you go from running tuberculosis programs in rural Bihar to studying at Oxford and eventually working with WHO Africa? There is no clean answer — and that is sort of the point.In this episode, I sit down with Dyuti Sen to talk about what a public health career actually looks like when you do not start as a doctor, do not have a linear plan, and refuse to pretend the journey was smooth.Dyuti studied economics, then entered the development sector through a social leadership fellowship in India. That fellowship dropped her into Dalsinghsarai in Bihar — not a posting you would find on a glossy career brochure. She spent five years there working on tuberculosis, community health delivery, and the enormous gap between what policy says on paper and what happens when a patient in a remote village needs treatment. She calls those years her real master's degree, and after hearing the details, you will understand why.We get into what public health work looks like day to day — the fieldwork, the project management, the bureaucracy. Why "free medicine" does not automatically mean people can access it. Why community health workers carry deep practical expertise that often gets overlooked. And why the distance between a well-designed health policy and its execution on the ground is where the real work lives.Then we talk about her Oxford journey. How she shortlisted master's programs, why scholarships were non-negotiable, what shifted after getting into International Health and Tropical Medicine, and how she later navigated the confusing world of WHO and UN applications. If you have ever stared at a UN job portal and thought "how does anyone actually get hired here?" — Dyuti walks through it with real specifics.This conversation is for anyone interested in global health careers, public health in India, international development pathways, WHO jobs, Oxford scholarships, or what it takes to build a meaningful international career from a non-medical background. No fluff, no fairy tale — just the real version of the path.Guest: Dyuti SenHost: Naman PandeyDisclaimer: Views expressed in this conversation are personal and do not represent WHO or any affiliated institution.Chapters:00:00 Dyuti's journey into public health03:52 Experiences in Bihar: a unique perspective06:39 Adapting to rural life and community dynamics09:14 A day in the life: from fieldwork to project management12:11 Challenges in public health: insights from the ground13:58 The importance of community health workers16:58 Lessons learned: the reality of public health work20:01 Battling tuberculosis in Bihar28:24 Navigating the application process for graduate studies36:09 Navigating scholarship applications and mindset39:19 India vs. the West: contrasting public health perspectives48:48 Journey to WHO: career path and lessons01:01:15 Understanding WHO's role in global health01:06:15 Future aspirations in public health
Transcribe →How to Vibe-Code 20 Production Apps in 13 Days - w/ Avi
3w ago00:52:05Tap to summarizeTwenty apps. Thirteen days. Some already making money.That's what Avi Pilcer actually shipped while most of us were still picking a domain name. And the wild part? He didn't do it alone. He built an autonomous system called System Zero that does the heavy lifting for him.If you've been told "AI is making it easier to build" but you still freeze at "what should I build, how do I ship it, who's going to use it?" — this episode unjams the whole pipeline.Avi's path is not a normal founder origin story. He went from a struggling engineering student to an electric violinist in China (yes, really) to leading AI innovation at Motorola — all before "deep learning" was on most people's vocabulary list.Now he runs a framework-agnostic orchestration system that ships apps, buys Google Ads, writes SEO blogs, and nurtures email lists. All of it runs 24/7. None of it needs him babysitting.Host Naman Pandey gets into the parts nobody actually explains:Why "meta thinking" is the one skill that still matters when AI handles the code and the copy.How an automated AI process picks which apps to build — so you stop drowning in a Notion doc full of half-baked ideas.A live demo of Fleet AI launching a business in real time (this part will probably make you close your laptop and rethink your week).The "prune and purge" method for killing dead apps fast, because shipping is only half the game. The other half is knowing when to walk away.And why Avi thinks money and traditional jobs are on borrowed time — plus what to actually do about it before that clock runs out.If you're an indie hacker or a solopreneur who keeps refreshing Twitter looking for permission to start — this is the permission. 🚀Built for the Peter Levels crowd and the Claude Code curious. Anyone tired of vibe coding their way into half-finished AI apps will find a real playbook here.Whether you're chasing your first profitable agentic AI workflow or trying to wire up autonomous AI agents that genuinely run without you, Avi hands over the full stack.This one belongs in the "watch twice, take notes" pile.⏱️ TIMESTAMPS00:00 – Building 20 Apps in 13 Days13:18 – Navigating AI Tools for Beginners19:19 – Demo of Apps26:45 – System Zero32:07 – Autonomous Operations and Self-Improvement36:11 – Integrating Multiple Projects and Use Cases37:59 – Distribution Strategies for App Success39:03 – Cost Optimization and Business Viability41:03 – The Future of Software Engineering in an AI World51:03 – Exploring System Zero: The Autonomous App BuilderSubscribe to Ready Set Do for more raw conversations with the people building what comes next.
Transcribe →How to Get Hired for Applied AI Roles in Fortune 500 Companies (Target Sr Data Scientist POV) - w/ Sowmya
3w ago00:55:00Tap to summarizePicture this: it's 2023, ChatGPT just dropped, every Fortune 500 suddenly needs an "AI strategy," and the job postings want 5 years of experience in a technology that's barely 18 months old. How does anyone actually get hired in that mess?Sowmya Podila figured it out. She's now a Senior Data Scientist on Target's centralized Generative AI team — basically the internal startup that decides which AI use cases get built, killed, or scaled across the entire company. Before Target, she was deep in the chaos: interviewing at Google and other Fortune 500s during the wildest hiring wave applied AI has ever seen.We pull back the curtain on what enterprise AI actually looks like from the inside. Not the LinkedIn version. The real one.She walks through how she prepped for GenAI interviews when nobody on Earth had three years of GenAI experience, why she picked Target over Google (the answer is more interesting than you'd think), and what Fortune 500 hiring managers are actually screening for in 2026 once the hype settled.Here's the part nobody tells you: knowing when NOT to use AI is the most valuable skill on her team right now. Every PM wants an LLM in their product. Most of those ideas should be a SQL query and a dashboard. Sowmya breaks down how her team plays gatekeeper — and why that judgment call is what gets you hired, promoted, and trusted with bigger budgets.We also get into the agentic AI question everyone's dancing around. Can AI agents actually run a Fortune 500 company? (Spoiler: no, and the reasons are way less sexy than Twitter would have you believe — it's data plumbing, not model capability.) She talks through building a multi-agent simulator at Target, why company size completely changes the AI adoption playbook, and the messy reality of getting clean data out of systems built in 2008.Then we go big picture. Utopian future vs. dystopian future. Will AI take your job? Sowmya gives an honest answer that doesn't pander in either direction, which is rare these days.If you're trying to break into applied AI, data science, or ML engineering at a large company — or you're already in and trying to figure out what to bet your career on next — this one's for you. We cut through the noise so you know what actually matters vs. what's just LinkedIn theater.Connect with Sowmya:LinkedIn: https://www.linkedin.com/in/sowmyapodila/Podcast site: www.readysetdopodcast.comTimestamps00:00 - AI Implementation Landscape in Fortune 500 Companies04:21 - The Evolution of AI Strategies in Enterprises07:20 - Interview Insights: Navigating the AI Job Market10:19 - The Current Landscape of AI in Enterprises13:28 - Understanding the Role of AI in Business Decisions16:24 - The Future of AI Agents in Enterprises28:01 - Building a Multi-Agent Simulator31:15 - Challenges in AI Data Utilization33:33 - The Impact of Company Size on AI Adoption40:02 - The Future of AI: Utopian vs. Dystopian Views45:45 - The Importance of Content Creation in AI Awareness
Transcribe →How to Get a UK Work Visa With No Lottery (H-1B Lottery Reject POV) - w/ Hari
Apr 1600:54:07Tap to summarizeHari Prasad Renganathan got rejected from the H1B lottery three times. Most people in that situation spiral. They scramble for a second master's, beg their company for an internal transfer, or start mentally preparing to go back home.Hari texted 10 YC startups, got callbacks from four of them, landed an offer as a Senior AI Engineer, and moved to London. The UK work visa took three weeks. No lottery.But here's the thing — that's just the final chapter of a pattern that's been repeating his entire career. Every time Hari hit a wall, he did the thing nobody else was willing to do.He applied to Columbia with a mechanical engineering degree from Tamil Nadu. No CS background. His GRE score was so average he deliberately withheld it from his application. He wrote an SOP that broke every template the admissions consultants swear by. He got in.After Columbia, he couldn't land an interview for four months. When a senior data scientist on his team quit, Hari walked up to the decision maker and said — and I'm quoting him here — "I'll be much cheaper than that guy." He got the full-time offer.Then he launched My Real Product, a community workshop where he helped data scientists and AI engineers stop building cookie-cutter Kaggle projects and start building actual products with real users. He put up a LinkedIn post expecting 20 people. 470 showed up.We also get into the AI conversation. Hari has a take on the whole "AI won't replace you, but people using AI will" thing. He disagrees. So do I, actually. He breaks it down into four layers of AI replacement and where you need to be if you want to be the last person standing. Whether you agree or not, it's the most specific framework I've heard anyone lay out on this topic.This conversation is for you if you're an international student navigating the US visa system, if you're trying to break into data science or AI without a traditional CS background, if you've been following every template and wondering why nothing is working, or if you just want to hear what happens when someone refuses to take no for an answer at every single turn.Timestamps, links, and ways to connect with Hari are all below.If this episode helped you think differently about even one thing, share it with somebody who needs to hear it.Site: readysetdopodcast.comHari: https://www.linkedin.com/in/hariprasad20/Timestamps:00:00 Introduction & Connection03:23 AI, Personal Branding, and LinkedIn Presence05:29 From Mechanical Engineering to Data Science10:11 Navigating the Columbia Application Process14:56 Life at Columbia University19:43 Transitioning from Student to Professional23:26 Entrepreneurship and Building MyRealProduct26:26 Problem Solving with Data Science29:38 Building a Data Science Community33:12 Rejection from H-1B Lottery and Exploring Career Paths45:43 The Future of AI and Its Impact
Transcribe →How To Crack Your First US AI PM Internship (With No Prior PM Experience, Purdue MEM POV) - w/ Aryan
Apr 900:36:59Tap to summarizeTwo weeks into my first semester in the U.S., I remember sitting at my desk refreshing LinkedIn like it was a slot machine. Apply. Refresh. Apply. Refresh. Zero replies.At some point you start asking yourself the obvious question: is this just how it works here… or am I doing it wrong?This episode is the answer I wish I had back then.I sat down with Aryan Vaidya — a Purdue MEM (Master of Engineering Management) student who pulled off something most people spend years chasing. He went from being a Data Engineer at Shell in India to landing an AI Product Manager internship in the U.S. by his second semester. No built-in network. No shortcuts. Just a very intentional strategy.And here’s the part that’ll probably annoy you (it annoyed me): a lot of the things people tell you to do… just don’t work the way they claim.Career fairs? Overcrowded.Generic applications? Ignored.“Wait your turn”? Doesn’t apply here.So what does work?Aryan breaks down why cold applying still works in 2026 — if you actually do it right. Not blasting 200 resumes. Not tweaking one bullet point and hoping for the best. He treated every application like a product problem. Who is this for? What do they care about? Why should they pick you?That shift alone changes everything.We go deep into his PM resume framework — what he calls the “So What?” test. Every line on your resume needs to answer that question. Not what you did. Not what tools you used. But why it mattered. (If your bullet can’t survive that test, it’s dead weight.)Then there’s the part most people avoid: reaching out directly.Instead of waiting for recruiter replies, Aryan messaged founders. Straight up. No overthinking. No long essays. Just clear intent and relevance. That’s how he landed his AI PM internship at a startup, and it completely changed his trajectory.We also talk about a move that sounds insane on paper — he dropped his initial coursework to double down on breaking into product. Risky? Yes. Random? Not really. It was a calculated bet on what would actually move the needle.So zoom out for a second.If you’re an international student in the U.S., trying to land a product management internship, or thinking about a career pivot from engineering to PM — what’s your actual strategy right now?Because “apply more” isn’t a strategy.This conversation is.You’ll walk away with a clear system for:Breaking into Product Management roles in the U.S. without a networkBuilding a high-converting PM resume that stands outUsing cold outreach and LinkedIn messaging to create opportunitiesPositioning yourself as a strong candidate even without prior PM experienceNo fluff. No recycled advice. Just what’s working right now.If you’ve been stuck refreshing that job portal (you know exactly what I mean), this one will hit differently.Watch the full episode and start playing the game the way it’s actually played.Timestamps:00:00 The Purdue MEM to PM Pipeline04:12 Career Journey and Transition to Product Management07:06 First Semester Experience at Purdue09:58 Networking and Job Application Strategies12:54 Resume Building and Interview Preparation16:08 Navigating the Startup Environment18:58 Insights from the AI PM Role21:58 Final Thoughts and Advice for Future Students
Transcribe →How To Publish 100 Podcast Episodes (10 Lessons I Wish I Knew on Day 1)
Apr 700:26:21Tap to summarizeTwo years ago, I was lying in an ER bed, scared enough to get brutally honest with myself.And the thing that bothered me most was not some giant life philosophy. It was embarrassingly specific. I had still not started the YouTube channel and podcast I had been talking about for way too long.That night became the reason Ready, Set, Do exists.Now, exactly two years after uploading the first episode on April 4, 2024, the show has hit 100 episodes. And this felt like the right time to say what I actually think about podcasting, not the polished creator-economy version people usually give you.Because the truth is, podcasting is hard in a very unglamorous way.The ROI math makes no sense for a long time. Discovery is brutal. The algorithm does not care how thoughtful your conversation was. A guest’s pedigree will often beat a better episode. A great thumbnail can hurt you if it attracts the wrong click. And if your audio is bad, none of the rest of it matters.In this 100th episode of Ready, Set, Do, I break down the 10 biggest lessons I learned from starting a podcast, growing a YouTube interview show, and trying to survive the first 100 reps without losing the plot.We get into why your guest’s family may be your best marketing engine, why “ex-Microsoft” or “IIT” can outperform raw insight on YouTube, why high CTR with low retention is a trap, and why podcast audio quality is still the one thing you should never cheap out on. I also talk about a branding mistake I made early with the “not-experts” framing, why episode 7 was the one where I actually found my voice, and why I have badly neglected solo podcasting even after 100 episodes.This is also a bigger conversation about building anything from scratch.What happens when the thing you are making is a little hard to categorize? What happens when your show is genre-agnostic by design, but platforms want clean boxes? What do you do when 95% of your watch time comes from non-subscribers, and every upload is really an audition, not a reunion?That is the real thread underneath this episode.So whether you are figuring out how to start a podcast, thinking about starting a YouTube channel, trying to improve your video podcast setup, learning interview podcast tips, or just wrestling with the early messy phase of making anything on the internet, this one is for you.No fake guru energy. No motivational poster fluff. Just the stuff I wish someone had told me before I started recording.Listen on Spotify, Apple Podcasts, or wherever you get your podcasts.www.readysetdopodcast.comTimestamps:00:00 A Life-Changing Checkup03:00 The Birth of a Podcast04:38 Lessons from 100 Episodes05:35 Lesson 106:50 Lesson 208:53 Lesson 311:06 Lesson 412:34 Lesson 515:32 Lesson 618:10 Lesson 719:21 Lesson 821:37 Lesson 924:15 Lesson 10Relevant topics: how to start a podcast, podcasting for beginners, YouTube podcast tips, 100 podcast episodes, podcasting lessons, brutal truth about podcasting, video podcast setup, content creator advice, how to grow a podcast on YouTube, podcast audio quality, solo podcasting, creator economy, starting a YouTube channel, overcoming fear of failure, Ready Set Do podcast, taking action, interview podcast tips.
Transcribe →How to Future-Proof Your Tech Career in the Age of AI Agents (3x UiPath MVP POV) - w/ Naveen
Apr 201:04:29Tap to summarizeEveryone's hyping autonomous AI agents. Your corporate IT department is quietly building a blacklist.That gap — between the hype and the reality inside enterprise walls — is exactly what this episode is about. Naveen Chatlapalli, a three-time UiPath MVP and seasoned .NET developer, has watched the automation industry reshape itself in real time. He's seen RPA go from a boardroom buzzword to a genuine enterprise backbone. And now he's watching Agentic AI arrive with all the confidence of someone who didn't read the security brief.So is traditional RPA actually dead? Short answer: not even close. Longer answer: complicated, and the complications are the interesting part.Naveen walks through how he got into UiPath before most people knew what robotic process automation was, what it takes to build a career in a field that keeps reinventing itself, and why the jump from rule-based automation to autonomous AI agents is less of an upgrade and more of a trust exercise nobody's prepared for.Then things get uncomfortable — in the best way.Open-source agent frameworks like OpenClaw sound great in a demo. In production, the enterprise AI security picture gets ugly fast. Naveen breaks down what prompt injection means in an enterprise context (hint: not a niche concern for someone else's team), how AI data exfiltration risks are being underestimated, and why major corporations are pausing agentic AI deployment while the vendor ecosystem scrambles to catch up.Claude Cowork, Anthropic's autonomous desktop agent, gets the same honest treatment. The capability is real. The AI compliance and enterprise trust story are still being written.Cost-effective AI solutions, emerging startups filling the trust gap, and the broader domino effect of enterprise adoption all come up too. This episode covers the full arc — from what Naveen's career trajectory tells us about where automation engineering is heading, to the personal habits that help him stay current in a field that moves faster than any job description can track.If you're an RPA developer wondering whether your skills still matter, a tech leader evaluating agentic workflows, or just someone trying to separate signal from noise in the AI agent conversation — this one's worth your full hour.👉 Follow Naveen on LinkedIn: https://www.linkedin.com/in/1aifanatic/00:00 The Rise of Agentic AI04:22 Evolution of AI in Customer Service07:12 The Future of Work and Job Evolution10:11 Naveen's Journey to UiPath13:09 Understanding .NET and RPA16:04 Building a Career in RPA19:00 Agentic AI vs. Traditional RPA21:50 Trusting Agentic AI in Enterprises25:02 The Domino Effect of Agentic AI Adoption28:02 Open Source Contributions and Frameworks34:51 Security Concerns in OpenClaw38:42 Navigating Risks and Best Practices39:47 Emerging Startups and Innovations44:39 Cost-Effective AI Solutions49:15 Enterprise Trust and Adoption52:22 Personal Growth and Reflection57:18 Staying Updated in a Fast-Paced World
Transcribe →How to Get a Job by Just Showing Up (Moving to SF With Nothing) - w/ Deep
Mar 2500:35:28Tap to summarizeMost people grinding through a tough job market send 300 applications and wait. Deep Suchak bought a one-way ticket to San Francisco.No offer. No contacts. No couch lined up past the first week. Just a read on where the opportunities actually were — and a conviction that showing up in person would do what a perfectly formatted resume never could.In this episode of Ready Set Do, Deep walks me through the whole story. The decision to leave, the first freezing weeks of uncertainty, the cold office walk-ins, and the moment the strategy actually started working. (And yes, there were plenty of moments where it really looked like it wouldn't.)Here's what makes his story worth paying close attention to right now: the job market in 2026 is brutal for recent grads. Remote-first hiring has made proximity feel irrelevant. Most career advice tells you to optimize your LinkedIn headline and apply through the portals. Spray and pray. Hope the ATS doesn't eat your file.Deep went the other direction entirely.Deep calls it proximity strategy — the idea that physical presence in the right city, combined with genuine cold outreach, creates a shortcut that no algorithm can replicate. It sounds almost too straightforward. And yet the results speak for themselves.We get into the full mechanics of how he pitched himself into rooms, how he approached networking when he knew absolutely nobody in the city, and how he mentally handled the long stretches where nothing seemed to be moving. There's a section on the GTM Engineer title that's genuinely clarifying if you've been seeing that term everywhere and still aren't entirely sure what it looks like day-to-day. We also dig into SEO strategy for early-stage startups — which came out of his actual work, not as a side tangent.What I keep coming back to after this conversation: the job search tips that actually work in 2026 are not the ones being taught in career centers. Cold outreach for jobs, done with a real point of view and a willingness to physically show up, still works. Relocating for a job before you have it locked down still works. The candidates getting hired right now are treating the search itself like a growth problem — not a waiting game.Deep solved his. And he's generous enough to share exactly how he did it.If you're a recent grad, early in your career, or just exhausted from sending applications into the void — this one's for you.🎙️ Ready Set Do | Host: Naman Pandey | readysetdopodcast.com—00:00 Navigating the Job Market: Strategies and Challenges02:17 The Power of Networking: Building Connections05:06 The First Month: Adapting and Overcoming08:05 The Interview Process: Preparing for Success11:07 Pitching Ideas: The Art of Growth Strategy13:58 The Role of a GTM Engineer: Demystifying the Title17:00 Reflections on Risk and Resilience19:47 The Importance of Persistence: Never Giving Up30:40 Implementable Steps for GTM Engineering34:44 SEO Strategies for Startups
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