Ashwin Kumar

🍁 Top Tech Openings for Early and Mid Career Professionals in the US 💡 Quick Tips to Break into Program & Product Management - 1️⃣ Understand the Roles: PM vs Program Manager: focus on product vision, strategy, and execution vs project coordination and cross-team delivery. 2️⃣ Build a Portfolio: Share case studies, side projects, or product experiments showing problem solving and impact. 3️⃣ Learn Key Frameworks: Agile, Scrum, roadmap planning, KPIs, and user centered design basics. 4️⃣ Highlight Transferable Skills: Leadership, communication, analytics, stakeholder management, and problem solving. 5️⃣ Network Smartly: Connect with current PMs and Program Managers, attend meetups, and ask for informational interviews. 6️⃣ Leverage Micro Credentials: Certifications or courses from Google, PMHQ, or Coursera can boost credibility. 7️⃣ Show Business & Tech Awareness: Understand market trends, company products, and relevant technology. 8️⃣ Tailor Your Resume: Quantify achievements, focus on impact, and align experience with job requirements. #TopTech Roles 👉 Software Engineer I - Markem-Imaje 🔗 https://lnkd.in/gQrZ5Jkd 👉 Full-Stack Engineer - BNY 🔗 https://lnkd.in/gXhBcRmS 👉 Sr Associate, Full-Stack Engineer - BNY 🔗 https://lnkd.in/gZ-B9GhC 👉 Sr Associate, Business Process Management Analyst - BNY 🔗 https://lnkd.in/gzkJure8 👉 Full-Stack Software Developer - Greenpixie 🔗 https//lnkd.in/gGV35nq5 👉 IT Intern - HHR 🔗 https//dsp.prng.co/mB0LxAb 👉 Sr Solutions Engineer - NetApp 🔗 https//dsp.prng.co/ZG2c8ab 👉 Sr. React Developer - Charles Schwab 🔗 https//dsp.prng.co/WSmVxNb 👉 Senior IT Business Analyst - PG&E Corporation 🔗 https//dsp.prng.co/h6bzTRb 👉 Lead Analyst - OneMain Financial 🔗 https//dsp.prng.co/LoesaWb 👉 Project Management Analyst - Guidehouse 🔗 https//dsp.prng.co/vh4ueOb 👉 Business Process Analyst - Guidehouse 🔗 https//dsp.prng.co/nxTjc-b #ProgramManager 👉 Program Manager I - Google 🔗 https://lnkd.in/dYXaTkRe 👉 Sustainability Program Manager - Microsoft Corporation 🔗 https://lnkd.in/dqvMkx6M 👉 Technical Program Manager - Apple 🔗 https://lnkd.in/d4yYzqKu 👉 Associate Product Manager - Zinnia 🔗 https://lnkd.in/dVaB4pFq 👉 Product Manager I - tastylive 🔗 https://lnkd.in/dmzMz2Sw 👉 Technical Product Manager - Telnyx 🔗 https://lnkd.in/d9KdGKVT 👉 Associate Product Manager - Modern Health 🔗 https://lnkd.in/dtA4knZK 👉 Product Manager - Lucid Motors 🔗 https://lnkd.in/dRdKD-M8 👉 Product Manager - Robinhood 🔗 https://lnkd.in/dHDpF_h2 👉 Product Marketing Manager I - Uber 🔗 https://lnkd.in/d7rSuQGY 𝐓𝐨𝐝𝐚𝐲'𝐬 𝐟𝐮𝐥𝐥 𝐥𝐢𝐬𝐭 𝐨𝐟 𝐫𝐨𝐥𝐞𝐬 𝐢𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐞𝐧𝐭. 📌 Want early access to tech openings like this before they go viral? Drop your email below 💬 or DM me for exclusive early access. 👀 Follow Arpita Rawal for daily US tech opportunities, plus updates in Data Science and AI.

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Last week, the inaugural launch of The AI Collective's North Dallas Chapter brought together AI professionals, data leaders, and technology executives for a comprehensive exploration of enterprise AI transformation, with a particular focus on the evolution from traditional AI to agentic AI systems. Dr. Geethamani Jakka (Capital One) - Cyber Data Engineer Linga Reddy Alva (Warner Bros Discovery) - Data AI Solution Architect MAHESH KUMAR GADDAM (Fedex) - Solutions Architect Naveen Nalajala (Dell Technologies) - AI Implementation Engineer Shiva Krishna K. (Broad Financial) - Senior Engineering Manager Key Speakers & Presentations Lestan D'Souza - Co-Founder & CTO at ReKnew Mantu Samadder - Head of Data and AI / Agentic Gen AI at Genpact Manish Jain - Vice President and Global Head of AI Architecture, Firstsource Surendar K Chintala - Specialist Leader at Deloitte Consulting LLP (Summary of Presentations in the comments below.) The event emphasized three key themes: -Beyond the Hype: Moving from buzzwords to real-world agentic AI capabilities and applications -Industry Transformation: AI fundamentally transforming industries through efficiency, innovation, and new business models -Survival Imperative: Adopting agentic AI as essential for business competitiveness and long-term survival Key Takeaways +Terminology matters: Clear definitions prevent costly implementation failures +Context is king: Enterprise AI success depends on sophisticated context engineering rather than simple prompt-based approaches +Governance first: Comprehensive oversight frameworks must be implemented from day one +Business alignment: Technology decisions must start with business outcomes, not technical capabilities +Cultural transformation: Success requires addressing organizational resistance through training and emphasizing human-AI collaboration The event successfully established the North Dallas Chapter as a hub for practical, enterprise-focused AI transformation discussions, combining theoretical frameworks with real-world implementation experiences and cautionary tales from the field. Exciting to see that AICamp, The AI Collective, and other organizations are bringing the AI community together in DFW

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Most people build one company. I focused on acquiring infrastructure across 1.8M+ assets. Not in one category. Across logistics. Across data. Across capital. Across digital territory. Across operational systems. And much more Because scale does not come from a product. It comes from stacked infrastructure. Logistics infrastructure gives you movement. Data infrastructure gives you intelligence. Capital infrastructure gives you optionality. Digital infrastructure gives you control. Operational systems give you execution speed. And much more Most founders optimise one layer. Serious operators integrate all layers. When you control logistics, you control speed. When you control data, you control insight. When you control capital, you control timing. When you control digital territory, you control narrative. When you control systems, you control consistency. That is leverage. Acquiring 1.8M+ infrastructure assets was not expansion. It was compression. Compression of dependency. Compression of delays. Compression of negotiation risk. Compression of uncertainty. If you depend on others for movement, you wait. If you depend on others for data, you guess. If you depend on others for capital, you compromise. If you depend on platforms for distribution, you obey rules you did not write. Dependency is expensive. Ownership is strategic. But infrastructure is heavy. It requires capital discipline. It requires governance. It requires maintenance. It requires long-term conviction. You do not acquire 1.8M+ assets emotionally. You do it with a thesis. Every acquisition must answer: – Does this increase structural control? – Does this reduce operational friction? – Does this compound positioning? – Does this strengthen the ecosystem? If not, it is noise. Infrastructure is not about looking big. It is about being difficult to displace. Products can be copied. Content can be replicated. Teams can be hired away. Integrated infrastructure is harder to replicate. Because it connects logistics, data, capital, and systems into one architecture. That architecture creates gravity. Talent moves toward stability. Capital moves toward structure. Opportunities move toward execution capacity. The future will not reward the loudest founders. It will reward the most vertically aligned ones. Those who own movement. Those who own intelligence. Those who own capital pathways. Those who own execution frameworks. If you are building long term, stop asking: “What can I launch next?” Start asking: “What infrastructure layer am I missing?” Once the layers connect, scale stops being effort. It becomes engineered inevitability.

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Today, I attended ProductCon organized by Product School, virtually. It was packed with powerful insights and honest conversations about how AI is reshaping the product landscape. One point that really stuck with me was about the often-quoted MIT study claiming “95% of enterprise AI projects fail.” Honestly in my opinion, that statistic feels outdated, maybe even irrelevant. Because in 2025, if you’re not incorporating AI into your business, your failure rate isn’t 95%… it’s 100%. The real risk isn’t in experimenting with AI, it is in ignoring it. Tim Simmons from Walmart shared an inspiring example of how they’ve embedded AI into their operations in ways they couldn’t have imagined before. The ROI wasn’t just impressive, it was immediate. It reminded me that when you focus on solving real business problems, AI isn’t a gamble, it’s a growth accelerator. We’re past the stage of “if”, the conversation now is all about “how.” At InfoBeans, we’re already helping our clients avoid that 100% failure by turning AI ambitions into measurable success stories. If you’re exploring how to make your AI journey successful, let’s talk! Reach out to me here or DM me. #AI #AITransformation

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As product manager roles are transmutating from a pure manager type role to a PM engineer type role, here's the checklist for you - 1. Everyday play and experiment with different AI tools, internally make a list on which model to use for what purpose (this step is critical, as this compounds). 2. Never use AI for the initial draft when you are writing something. Writing brings you detailed clarity and in this AI age, clarity is king. Finish up your draft with AI, but start manually. 3. Always pay and use the frontier model. Don't cut corners here. The output of an Opus 4.6 is drastically different from sonnet when it comes to complex tasks. Having said that, develop a mental map on which model to use for what complexity of tasks. Otherwise you will just burn tokens for simple tasks. 4. When learning or understanding a concept, be deliberate and progressive when using the AI tools as well. For example, start with ChatGPT, take that output, understand it, add your own thoughts and then go to claude. Again point number 3, do apples to apples. If you are using the free tiers, treat all the free tiers as equals. 5. Learn to write agent native specs. Agent native specs are living docs which live in the repo, which the agent has direct access to. Take care of the smaller nuances. For example - If your team is using a context window of 100k models, then don't write specs which are too large. Eventually the agent will run out of context and start hallucinating. 6. When you have developed a strong intuition about points 1-5, then and only then build. The world is moving away from a PRD driven build environment to an agent spec driven build environment. Internalise that change. Change your workflow to adapt to it. 7. Build prototypes with the speed of thought, but remember these are prototypes and not actual production code. The prototype to production code journey is a bit far along. But that doesn't mean, you don't build, build your ideas quickly. Again the idea here is for you to develop enough intuition and judgement so that you can keep up with the agent. 8. As we are shifting from "vibe coding" to "agentic engineering", please remember this shift is going to be chaotic. Remember the world required 1-2 yrs to fully understand what actually is an "agent". So don't get overwhelmed, don't get any FOMO. Remember there's still a lot of time for the world to make this transition. All you can do is prepare better. Finally, anyone who is transitioning from a traditional PM to an AI native PM builder, remember, "chaos is a ladder". Be curious, be eager to learn, be adaptable, be flexible. Spend a lot of your time with the AI tools, stress test them, understand their limits, understand their context window and memory. This is in no way any exhaustive list (in fact I would argue this is a beginners list). But as you climb each stair in this ladder, you will develop enough intuition to move even further.

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How do you groom your Critical Thinking skills to do better in your product management interview? Critical thinking is integral to effective product management. It empowers you to make informed decisions, solve complex problems, and navigate uncertainties. Here’s why critical thinking is crucial and how to showcase it during your interviews. Why Critical Thinking Matters? Critical thinking involves analyzing information objectively, evaluating options, and making reasoned decisions. It’s essential for identifying root causes of issues, assessing risks, and crafting strategic solutions that drive product success. How to Exhibit Critical Thinking in Interviews... 1.⁠ ⁠Structured Problem Solving Example Demonstrate how you approach problems methodically. "When faced with a sudden drop in user engagement, I used a structured problem-solving approach: defining the problem, gathering data, identifying possible causes, and testing hypotheses. This led to identifying a UX flaw and implementing a fix that restored engagement levels." 2.⁠ ⁠Data Analysis and Interpretation Example Highlight your ability to analyze and interpret data. "In a previous role, I used data analysis to understand user behavior patterns. By segmenting the data and identifying trends, I uncovered key insights that informed our feature prioritization, resulting in a 10% increase in user retention." 3.⁠ ⁠Evaluating Alternatives Example Show how you evaluate different options before making a decision. "During a product development phase, we had to choose between two technologies. I conducted a thorough evaluation of both options, considering factors such as scalability, cost, and team expertise. This critical analysis led us to select the technology that best aligned with our long-term goals." 4.⁠ ⁠Anticipating Risks and Mitigating them Example Illustrate your foresight in anticipating and mitigating risks. "Before launching a new feature, I conducted a risk assessment to identify potential issues. By developing a mitigation plan that included additional testing and user feedback loops, we minimized post-launch issues and ensured a smooth rollout." 5.⁠ ⁠Making Informed Decisions Example Emphasize your decision-making process. "When deciding on the product roadmap, I synthesized inputs from various sources—market research, user feedback, and business objectives. This comprehensive approach enabled me to make informed decisions that balanced user needs with strategic priorities." Remember=> Exhibiting critical thinking as a product manager shows that you can navigate complexities, make sound decisions, and drive successful outcomes. By sharing specific examples of your analytical and problem-solving skills, you’ll demonstrate that you’re equipped to handle the challenges of product management. #ProductManagement #CriticalThinking #CareerAdvice #ProblemSolving #InterviewTips #AspiringPMs #ProductManagementJourney

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A big shift is about to happen: Global talent is being rejected left right center. The US is contemplating the visa H1B fee of $100K per employee, this could geographically shift the tech world in the coming decade, also disrupt the tech being built currently. Contrary to general belief, US has been built by attracting the best people from all across the globe - through education as an entry point and jobs as the offer. Having the best minds to work helped them be the largest economy. Businesses don’t run on employees of any particular ethnicity or race or religion, they run on the merit of people. And by default talent from the east is cheaper than the west (for the same value delivered) This if implemented, presents a big opportunity for India for coming decade with nearly 71% H1B visas being granted to Indians moving to the US. - As it will lead industries to move value tech to other geographies where the costs are favourable - Would help India retain some of the beet talent that goes abroad in search better opportunities But the key piece here is: Short term it isn’t great for all such employees cuz they are stuck in limbo. - Almost forced to stay in US - being laid off or - Not granted a visa (forcing return) This would change their lives forever with no fault of their own but just global politics and agenda (whether right or wrong I am not an expert) Indian industry can’t accomplish everyone short term, let alone meet their salary expectations; this would lead to a lot of chaos in tech hiring and jobs for a lot of affected individuals- my prayers go to them. #GlobalTalent #Migration Ps: pic at UK border when I visited, crowded as ever. (Not sure if they will follow suit)

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Having helped process H-1B visas for people over the years, I can say the program has been an important factor in the growth of the U.S. technology sector. The visa enabled firms to bring in highly skilled workers from abroad during periods when there were simply not enough qualified professionals available domestically. In 2010, I started at a recruitment agency focused on telecom supporting companies like AT&T, Verizon, Sprint, and T-Mobile in their bids to rollout new antenna tech. The industry was racing to deploy AT&T/UMTS, VZW/CDMA, and TMO/HSPA+, and soon after, LTE. During the nationwide rollouts of 3G and 4G networks in the 2000s and 2010s, the U.S. faced a shortage of RF engineers in areas such as performance, design, and optimization. According to the Dept. of Labor, the number of electrical and RF engineers being trained at U.S. universities was not keeping pace with demand and by the mid-2000s, fewer than 10,000 students were graduating with advanced degrees in electrical engineering. Many of these gaps were filled by engineers who came to the U.S. through the H-1B, particularly from India and China. Without that talent, the U.S. would likely have faced delays and risked falling behind in global telecom deployment. Not just in Telecom, H-1B visas have been heavily concentrated in technology across many sectors. In 2022, about 70% of all H-1B approvals were for computer and engineering roles, and nearly two-thirds of H-1B holders worked in the technology sector. Another fun fact, over half of U.S. “unicorn” startups (valued at $1B+) had at least one immigrant founder, many of whom first arrived on H-1B visas. In the case of telecom, it enabled the U.S. to keep pace with critical infrastructure rollouts, ensuring leadership in mobile communications. Will the $100k fee slow foreign hiring, or will big tech companies just pay the premium?

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