AswinKumar Chandra Sekaran

🚗➡️💡 Automotive Engineers → Semiconductor Roles In the last few weeks, ~45 professionals from the automotive domain joined our Interview Prep Bootcamp. Almost every DM I receive says the same thing 👇 “I don’t see long-term growth in automotive anymore. The market feels saturated.” If you’re feeling stuck in automotive and are planning to switch to semiconductor companies, there are 2 proven technical paths that actually work 👇 🔁 2 Clear Transition Paths to Semiconductor Companies 1️⃣ Linux Kernel & Device Driver Path Best suited if you want to move into: SoC / BSP teams Linux Driver Development System Software roles in semiconductor firms What you must master: Strong C programming & memory concepts Linux Internals (process, memory, scheduling) Device Drivers (char, platform, GPIO, I2C, SPI, interrupt handling) Debugging using logs, gdb, kernel traces Real interview-level problem solving 2️⃣ RTOS-Focused Path Ideal if you want roles in: Firmware / Embedded Software teams Safety-critical & real-time systems MCU-based product teams What you must master: Embedded C (deep understanding, not basics) RTOS concepts (tasks, scheduling, IPC, memory) Timing analysis & debugging Practical coding + interview questions 🎯 Why Most People Fail to Switch Not because of lack of effort — but because of: ❌ Random learning ❌ No interview structure ❌ Weak fundamentals in C & OS concepts ❌ No guided problem-solving practice 🧠 How Our Interview Prep Bootcamp Is Structured This is not another theory course. We follow a structured interview-first roadmap covering: ✅ C Programming (interview depth) ✅ Coding fundamentals & problem solving ✅ RTOS concepts (practical + interview focus) ✅ Linux Kernel & Driver fundamentals ✅ Real interview questions + mindset ✅ Guidance on how semiconductor companies evaluate you This structure is exactly why automotive professionals are able to confidently target semiconductor roles. Bootcamp Link : https://lnkd.in/g63fzCPq 📩 Confused Which Path Suits You? If you’re unsure whether Linux Kernel / Drivers or RTOS is right for you: 👉 DM me We’ll discuss your background, experience, and goals and I’ll guide you honestly. 🔗 Bootcamp link in comments / profile. #EmbeddedSystems #AutomotiveToSemiconductor #LinuxKernel #DeviceDrivers #RTOS #EmbeddedCareers #SemiconductorJobs #CProgramming #InterviewPreparation #CareerSwitch

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Key Aspects of TPM Role 1. *Strategy (Why)*: Defining the purpose and direction behind the work, aligning with business objectives. 2. *Execution (Predictability in Chaos)*: Breaking down work into manageable tasks, managing dependencies, and driving progress to meet timelines and budgets. 3. *Influencing (Collaboration and Conflict Resolution)*: Building consensus, negotiating, and driving stakeholders towards a common goal. Tools and Techniques - *Documentation*: Project plans, meeting notes, and status reports. - *Follow-ups*: Regular check-ins, progress tracking, and issue escalation. - *Analysis*: RAID (Risks, Assumptions, Issues, Dependencies) log, metrics, and data-driven decision-making. - *Program Management Tools*: Gantt charts, Jira, Kanban boards, dashboards for visibility and tracking. #PM #TPM #ProjectManagement #ProgramManagement #Agilemethodlogies #Jira

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I learned a lot from Mukund C. talking about Rivian's RAP1: - Why autonomy is so demanding for the SoC - Why custom silicon over merchant - TSMC’s automotive process - Build vs. buy decisions - Sizing 800 TOPS of compute https://lnkd.in/gM_MhmiH Check it out! Chapters: (0:00) Introduction (0:31) What does an autonomous vehicle have to do? (2:52) The R2 sensor suite and 5 billion pixels per second (4:29) Sensor fusion and the large driving model (7:24) Real-time and safety constraints (8:28) Response time, determinism, and ASIL D (11:51) TSMC N5A and automotive process maturity (14:32) Determinism: latency budgets and repeatable outputs (16:06) Inside RAP1: the building blocks (18:52) Build vs. buy: custom NNE and RivLink, licensed ARM cores (21:10) RivLink and designing for extensibility (22:28) Physical AI and Mind Robotics (24:10) How much compute is enough? Sizing 800 TOPS (28:10) Why custom silicon over merchant? (33:35) Shift left and the benefits of vertical integration (34:43) Team and timeline: ~2.5 years to silicon (37:27) Celebrating RAP1 and what’s next

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#DigitalManufacturing #EngineeringLeadership #EBITDA #OperationalExcellence #CADAutomation After 24+ years in BIW and manufacturing, one insight stands out consistently: A very small portion of design decisions drives a disproportionate share of project outcomes. In fact: 👉 Design accounts for ~5% of the Overall Project value but controls nearly  80% of the total project cost Yet, this is exactly where preventable errors still creep in. At the CAD level, we continue to see recurring issues: *Hole mismatches during assembly *Incorrect hole sizes affecting fitment *Part interferences missed in design *Wrong material specifications *Missing or incorrect hardware Individually, these may seem minor. But their impact is not. 💰 The Hidden Cost These design-stage mistakes typically translate into: 👉 3–4% of overall project losses Driven by: *Wrong parts being manufactured *Rework across design, tooling, and production *Late-stage engineering changes *Supplier and shopfloor disruptions For large programs, this is not a small number — it directly hits margin and EBITDA. 💡 The Core Problem Most organizations still rely heavily on: Manual checking and downstream validation Which means: Errors are detected late Corrections are expensive The same mistakes repeat across programs 🔍 The Strategic Shift: Control Errors Upstream Leading organizations are changing this by: Embedding intelligence directly into CAD through customization ✔ Rule-based validation systems ✔ OEM and program-specific standards built into design ✔ Automated detection of geometry and specification errors ✔ Real-time feedback to designers 🚀 The Real Unlock The most effective CAD customizations are built from: Quality issues identified at the user level Every: Hole mismatch Interference issue Wrong material callout Missing hardware definition …is not just a correction. 👉 It is a pattern 👉 It is a rule waiting to be codified 👉 It is a preventable cost for the next program 📈 Business Impact When done right, this approach delivers: Higher first-time-right engineering Reduction in rework and iteration loops Lower program volatility Protection of margins at scale 🔑 The Leadership Question Are we still relying on people to catch errors… or Are we building systems that ensure errors don’t pass through at all? The future of digital manufacturing is not just about automation. It is about ensuring that the 5% of design decisions that control  80% of cost… are always right. Would be great to hear from industry peers: How are you controlling design-driven losses in your programs today?

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🌟 In my two decades  of professional journey, I’ve had the privilege of working under diverse styles of leadership.Some leaders were brilliant strategists, always hunting for new opportunities and envisioning where the organization should be in the next 5 years. Others were exceptional at execution, bringing people together, aligning resources, and ensuring that strategies translated into measurable results.But the real magic happens when these two qualities merge in one leader: The strategist provides vision and foresight. The executor ensures priorities are met with teamwork and precision. One is a taskmaster pushing for excellence, the other balances it with wit and empathy 🔑 This combination works like a well-oiled machine—delivering results that often exceed expectations. 💭 My takeaway: Strategy sets the direction, execution brings it to life. Together, they can accelerate growth and transform organizations. 👉 What do you think—are great leaders born strategists, executors, or a blend of both

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💡 Beyond the Quick Fix: The 5 Whys ​I am currently reading "The Lean Startup" by Eric Ries. While often labeled a "tech" book, its insights into the 5 Whys are essential for those of us in the manufacturing trenches. ​Eric writes: ​"Behind every seemingly technical problem is actually a human problem waiting to be discovered." ​In Tier 2 injection moulding, it is easy to stop at the first "Why" and blame a machine setting or an operator error. But true Quality Assurance requires us to dig deeper. ​If we don't reach the 5th Why, we aren't fixing a problem; we are just delaying its return. ​The goal: Stop fixing symptoms. Start fixing the processes that allow them to happen. ​#QualityAssurance #InjectionMoulding #LeanStartup #5Whys #RootCauseAnalysis #Manufacturing #ProcessImprovement #ContinuousImprovement

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👩‍💻 Are Zoox and Waymo Automotive Companies or Tech Companies? Five years ago, I made a significant shift in my career: moving from supporting some of the world’s most innovative high-tech customers to working in the automotive industry. It wasn’t just a change of industries— it was stepping into a space where hardware meets AI, where vehicle safety meets software reliability, and where two massive industries are colliding to redefine mobility. As I immersed myself in this new world, one question kept coming up — both for the companies leading the charge and for professionals like me: 🤔 Are companies like Zoox and Waymo automotive companies, tech companies, or something entirely new? Zoox, acquired by Amazon in 2020, is building a fully autonomous vehicle from the ground up — not retrofitting existing models. This places it firmly in the automotive realm for engineering, safety, and manufacturing. But its true edge is its software stack: perception, localization, motion planning, and fleet orchestration — capabilities traditionally owned by robotics and AI firms. Waymo, born from Google’s self-driving project, took a different approach. By partnering with OEMs like Jaguar and Chrysler, it integrates its autonomy platform into existing vehicles. This positions Waymo less as a manufacturer and more as a mobility technology platform, akin to how Android powers various smartphone brands. 🤔 Why Does this matter you might ask? This distinction influences how these companies operate: 👉 Capital Structure: Automotive requires heavy investment in factories and regulatory approval; tech prioritizes software, cloud, and data infrastructure. 👉 Business Models: Automakers sell vehicles; tech companies license platforms or charge per use. 👉 Regulation: Automotive focuses on crash safety and production standards; tech focuses on cybersecurity, AI ethics, and data compliance. 👉 Talent: Automotive attracts mechanical and operational expertise; tech attracts AI, software, and cloud talent. Zoox integrates both worlds — building vehicle + software. Waymo focuses on scaling autonomy software across partners. 🤔 The real answer may not be “automotive” or “tech.” Both companies are moving toward Mobility-as-a-Service — deploying autonomous fleets that could reshape urban transport. Ownership gives way to orchestration: fleets, logistics, and city-scale mobility infrastructure. The automotive and technology industries are no longer separate lanes — they’re converging into a new mobility ecosystem. Zoox and Waymo sit at the center of this transformation, challenging legacy categories. ✅ The question of automotive or tech? is certainly outdated. The answer might be: both — and something more. #AutonomousVehicles #MobilityInnovation #AutomotiveIndustry #MobilityTech #SoftwareDefinedVehicles #FutureOfTransportation #MobilityAsAService #InnovationStrategy

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Language is the most powerful tool for communication. It connects intent with understanding. In engineering, GD&T is that universal language - one that translates design intent into a precise product outcome, leaving no room for ambiguity across design, manufacturing, and quality. Last week, I had the opportunity to train 23 product engineers on GD&T Basics and Intermediate concepts in Bangalore (18–23 Dec 2025). What made the experience truly rewarding was the impact: Overall feedback: 4.5 / 5 Pre-assessment: 35.6% Post-training assessment: 83.2% Seeing this transformation reinforced a simple truth: "When the language is taught clearly, intent turns into impact". Feeling happy, grateful, and satisfied with the learning journey so far—and even more excited to take it forward. Looking ahead to Session 2: GD&T Applications & Tolerance Stack-up, where this language will be applied to real engineering challenges. Because mastering the language is only the beginning using it effectively is where excellence truly begins. Co-trainer Gopalakrishnan Sekar

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🚗 Execution Constraint and Bottleneck: Lack of Robustness Thinking — Lessons from Autonomous Vehicle Development Organizations today invest heavily in Quality, Reliability, Lean Six Sigma and Operational Excellence initiatives — streamlining processes, eliminating waste, and maximizing throughput. These efforts are essential, but they often expose a deeper, less visible bottleneck: 👉 The lack of Robustness Thinking. This topic recently motivated an engaging discussion at professional Reliability and PHM (Prognostics and Health Management) meetings, where many agreed that traditional approaches in Lean Six Sigma, Quality, and Reliability alone cannot ensure system excellence. During the development of an autonomous driving vehicle, the engineering team mastered Lean execution. Testing cycles were rapid. Hardware assembly was highly efficient. Yet when prototypes hit real-world roads — heat, rain, vibration, electromagnetic interference — performance degraded. Sensors drifted, algorithms struggled, and the system faltered under variability. The team had optimized how value flowed, but not what flowed — the design’s ability to transform and manage energy and information robustly under unpredictable real-world conditions. No amount of process efficiency could compensate for the lack of Functional Energy Flow integrity — the robustness of how energy and information traveled through the system. 💡 Robustness Thinking is the missing link. It focuses on maximizing Functional Energy Flow — ensuring that systems transform and transmit energy efficiently and predictably across all conditions. By integrating Robustness Thinking early in concept and detailed design, organizations can: ✅ Eliminate or reduce design-induced variation and early “birth defects.” ✅ Strengthen reliability, quality, and safety at the source. ✅ Empower Lean and Quality processes to operate at their full potential. In short: Lean makes the flow faster. Robustness makes the flow stronger. Together, they remove execution bottlenecks and create sustainable performance. To achieve Overall Flow — fast, stable, and reliable performance — organizations must evolve from Lean alone to Lean + Robustness Thinking, where efficiency meets resilience and systems achieve true excellence by design. #EngineeringManagement #RobustDesign #Reliability #Quality #LeanThinking #OperationalExcellence #SystemsEngineering #ContinuousImprovement #AutonomousVehicles #IEEE #PHM #UniversityofHouston #LeanSixSigma #ASQ

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