Engineering Career

🔮 "AI and Big Data aren’t just trends — they’re the backbone of tomorrow’s economy." By 2030, the most valuable skillset won’t be just technical — it’ll be adaptable. Are you ready? According to the World Economic Forum’s Future of Jobs Report 2025, AI and Big Data skills are projected to see an 87% net increase in demand globally by 2030 India, with its rapidly expanding digital economy, is uniquely positioned to capitalize on this transformation. ▶️ 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗧𝗲𝗰𝗵 𝗧𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆:  • The Indian tech industry is targeting $500 billion in revenue by 2030   • Demand for AI, Big Data, and Cybersecurity specialists is expected to grow by over 60%.  • Nearly 1 million young Indians enter the workforce every month — a demographic dividend that can become a global advantage if upskilled effectively. 📈 𝗧𝗼𝗽 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗥𝗼𝗹𝗲𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮:  • Big Data Specialists: Critical to managing the explosion of data across industries.  • AI & Machine Learning Specialists: Driving automation, personalization, and innovation.  • Security Management Specialists: Safeguarding complex digital ecosystems. 🧠 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘁𝗼 𝗙𝘂𝘁𝘂𝗿𝗲-𝗣𝗿𝗼𝗼𝗳 𝗧𝗵𝗲𝗶𝗿 𝗖𝗮𝗿𝗲𝗲𝗿𝘀:  • Big Data Tools (Spark, Hadoop, Kafka)  • AI & ML Integration (MLOps, model deployment)  • Cloud Computing (AWS, Azure, GCP)  • Cybersecurity Awareness  • Analytical & Creative Thinking  • Technological Literacy & Agility 💡 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗧𝗼𝗼:  • Resilience  • Flexibility  • Systems Thinking  • Collaboration across disciplines 🌍 Why This Matters: With 39% of core job skills expected to change by 2030  2, the future belongs to those who can adapt, learn, and lead in a tech-first world. Data Engineering isn’t just surviving — it’s evolving into one of the most strategic and high-impact roles of the next decade. 📘 Dive into the full World Economic Forum here: https://lnkd.in/gMExtKHr #Data #Engineering #AI #BigData

A collection of learnings from my 15-year Software Engineering career at companies like Meta, Pinterest, and Walmart. 1. To learn how to code, you must write code in an unstructured environment. Tutorials can help initially, but don't get stuck in tutorial hell: these engineers can't actually solve problems. 2. The only way to learn how to write good code is to write a bunch of terrible code first. It is fundamentally about the struggle. 3. Debugging is effectively playing a game of detective. Becoming an expert debugger in a large, complex codebase will make you extremely valuable to any company. 4. For software engineers, most of what you learn in school won't be relevant on the job. The biggest value of a university education is your network. Invest in getting to know students and faculty. Don't worry too much about grades. 5. Networking is about building long-term relationships built on trust and value. Give more than you take and your network will grow rapidly. Remember this phrase: "Your net worth is your network." 6. Everyone in tech faces imposter syndrome. Consider imposter syndrome as an opportunity to learn from people who are further along. Actively seek out feedback and talk to people. 7. Tech interviews are immensely broken and your interviews will probably differ from your job. View interviews as a learning opportunity where you get to meet some other cool, smart people. 8. Realize that the average person will spend < 10 seconds scanning your resume. No one is as interested in you as you, so you need to keep things short. Your resume should be 1 page long. 9. Feedback is the secret to rapid career growth. Make it easy for others to give feedback by introspecting and asking for specific parts of your behavior. A lazy “Do you have any feedback for me?” will often be met with a similarly lazy “Nope, you’re doing great!” 10. If you're not sure what company to join, go to a larger, well-respected company (FAANG) as your first job. Junior engineers benefit from the consistency and stability of Big Tech. 11. Onboarding is a magical time when you get a free pass to ask as many questions as possible, request people's time, and build foundational relationships. Work with a sense of urgency when you're new to a company. 12. The relationship with your manager is the most important relationship you'll have in the workplace. You should proactively drive meetings and feedback with your manager; don't wait for them. 13. Getting promoted as an engineer is not just about skill or output. You also need scope and trust. Most promotions are deliberately planned months in advance. If a promotion is important for you, bring it up with your manager well in advance. 14. Most engineers don't negotiate their offers, but they should. The most important tool for negotiation is leverage. This means competing offers. I put this all together in a 1.5-hour video here: https://lnkd.in/gAH4Q2pD

Mechanical, hardware, and chemical engineers are among the hardest-to-fill/hardest-to-hire roles for climate tech companies, with time-to-fill times longer than even machine learning engineering roles. ClimateTechList teamed up with data scientist/engineer Jason Zou to analyze our dataset of ~60,000 job posts from 900 climate tech companies posted in the last 6 months. Specifically, we found that the time-to-fill for the following roles were: - Sales: 31.9 days - Marketing: 35.9 - Analyst: 36.0 - Design: 38.5 - Data Science: 40.3 - Product Management: 41.5 - Operations: 42 - Electrical Engineer: 47.1 - Software Eng: 48.2 - Machine Learning Eng: 48.3 - Mechanical Eng: 49.0 - Hardware Eng: 50.2 - Chemical Eng: 51.5 Engineering jobs associated with physical production are hard to hire, namely mechanical engineering, hardware engineering, and chemical engineering, all of which take almost 2x as long to fill (50 days) as sales jobs. Even machine learning engineering positions, in high demand from the AI boom, are filled at a slightly faster rate than these 3 positions Possible reasons for this effect - many of these jobs require in-person work, which makes job matching jobs to candidates inherently more difficult - Federal legislation of the last few years- Bipartisan Infrastructure Law, Inflation Reduction Act, CHIPS Act are all driving massive investments into U.S. physical infrastructure and manufacturing. These investments disproportionally require talent with physical-product engineering skills more than software engineering skills. 👉 For more insights on hiring trends by company, country and climate tech vertical, see our latest climate tech hiring trends report here: https://lnkd.in/gpMCaSZ6 #climatetechlist #decarbonization #energytransition #chemicalengineering #mechanicalengineering #hardwareengineering #hiringtrends

After appearing in 20+ interviews, I have figured out how much Coding skills exactly required for each of Data Scientist, ML Engineer and AI Engineer roles. Do not get confused — these are separate roles and requires different coding expertise. 1. Data Scientists (product/analytics roles): - SQL is non-negotiable. - Python scripting + Pandas/NumPy are your daily tools. - DSA easy-medium (Leetcode enough!) Interviews often include: • SQL case studies • Data wrangling challenges (I got it many times!) • One Leetcode easy/medium — often string or array manipulation 2. Machine Learning Engineers: - You're expected to think like an engineer and a data scientist. - Writing production-quality code matters. - You’ll be tested on coding patterns, not just scikit-learn usage. Interview Expectations: • Leetcode medium (sometimes hard - but less chance) • Algorithmic thinking (e.g., optimizing training loop performance) • ML system design (batch vs streaming, deployment strategies) 3. AI Engineers / Applied Scientists: - Especially in LLM/Deep Learning-focused teams, system design + performance-aware coding is key. In this role you’ll deal with: - Large-scale data - GPU memory optimization - Custom training loops - Vector search, graph traversal, and more Coding rounds often include: • DSA-heavy problems (graphs, trees, recursion, DP) • Code optimization tasks • Python internals, multi-threading + processing, OOPs, memory & complexity analysis Coding is totally non-negotiable in every case. No matter how much theoretical knowledge you hold, if you can't solve live coding in interview you are straightaway rejected. For system coding I have started writing ML system design articles, feel free to check-out. [Link in comment]

The Complete DevOps Learning Roadmap for 2025 Want to break into DevOps? Here's your comprehensive learning path to master modern DevOps practices, based on my experience in the field: Foundation (Start Here): - DevOps Prerequisites - Networking fundamentals - Linux essentials - Shell Scripting basics Version Control & Collaboration: - Git & GitHub - Understanding repositories - Collaborative development practices Containerization & CI/CD: - Docker containerization - Jenkins for automation - Artifact Repository Management Cloud & Infrastructure: - Choose your cloud platform (AWS/Azure/GCP) - Infrastructure as Code with Terraform - Kubernetes orchestration - Helm for package management Automation & Monitoring: - Configuration management with Ansible - Monitoring with Prometheus & Grafana - YAML for configuration Database Knowledge: - Basic database concepts - Database management - Backup and recovery Don't try to learn everything at once. Master each component before moving to the next. Practice with real projects as you learn. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

Engineering research and development-related (ER&D) hiring has jumped almost 60% over last year, reports The Economic Times, citing data from Teamlease. Driving this trend is the demand from the manufacturing and automotive industries and global capability centres (GCC) of multinational companies and service providers. The ER&D sector employs around two million people in the country, according to industry estimates. Manufacturing and automobile companies with existing manufacturing setups in India are extending their R&D, technology, and design centres across the country. The result has been an increase in demand for contract employees in the field as companies setting up GCCs and centres of excellence (CoEs) look for talent on a trial basis, said Sunil C, CEO at Teamlease Digital. Companies have also been keen on converting contract roles to full-time ones. Top profiles in demand across IT services and GCCS include embedded C, cad/cam, automotive domain tools, and data engineering. What makes the ER&D sector a meaningful career prospect for professionals? Entire teams within a geography get to work on larger and more critical parts of projects, said Snehil Gambhir, Partner, Director-Transformation at BCG India, adding that automotive, aerospace, electronics, and semiconductor sectors are driving the demand in India. He also says companies are looking at India to build an alternative supply chain and delivery pool. The space constituted around 16% of the $245 billion Indian technology sector revenue, and grew faster over the previous year compared to the overall growth, as per FY23 Nasscom data. The demand for talent is also expected to grow at a compound annual rate of 12-15% over the next five years, according to Rohit Gupta, Head of Technology Center India at Thyssenkrupp. Source: https://lnkd.in/ewWR452G ✍️: Isha Chitnis 📸: Getty Images #AutomotiveIndustry #Engineering #ResearchandDevelopment #Aersopace #Manufacturing

Demand for software engineers is as bad now as it was during the peak of the pandemic. In 18 months, the number of data engineering job openings on LinkedIn has been cut in half. It’s not the end of technical roles, but the data shows demand is changing. Trying to replace engineers with low-code tools and #AI code generators fails. However, most platforms now support technical and nontechnical co-development environments. A new type of technical role has gained traction in businesses. Smaller software and data teams build frameworks and tools for nontechnical developers on a co-development platform that’s available to anyone in the business. Meta and JPMC implemented enterprise-wide co-development platforms and are seeing massive benefits. One or two technical resources are embedded into the nontechnical team to support their development efforts. Solutions are developed faster and more closely meet customer and business needs because domain experts build them. A few advanced R&D teams still operate in the business but focus on building larger, more innovative products. Teams supporting incremental features and internal operations initiatives are going away. While demand is falling in some areas, it’s rising for the embedded, business-facing technical roles. There are three levels: 1️⃣ Domain Expert Technical ICs: Value-centric #data engineers, data analysts, and software engineers who support nontechnical developers and are embedded into their organizations. 2️⃣ Product Manager Engineers: Technical capabilities with deep product strategy expertise. They know what to build and can implement high-value features independently or with a team. 3️⃣ Technical Strategists: Technical experts who work with executive and C-level leaders. They bring data, models, and rapid product development capabilities to the strategy planning and implementation processes. I have taught data and AI strategy, #ProductManagement, and value-centric capabilities to technical ICs for 8 years to meet today's demand shift. Technical roles are evolving, and amazing opportunities exist for people who adapt.

As more Australian companies move from experimenting with artificial intelligence to deploying it at scale, hiring for AI Engineers is surging. AI Engineer was ranked the fastest-growing job in LinkedIn's Jobs on the Rise report, with experts saying the demand is being fuelled by urgency. "Every leader is either challenging their current 'AI strategy' or scrambling for one, which has led to a surge of vague 'AI Engineer' roles that nobody, not even the hiring company, can properly define," says Ellis Taylor, Director at tech recruitment consultancy Real Time. He explains the "smartest demand" is coming from sectors with tangible problems, such as healthtech companies applying AI to diagnostic imaging or fintech firms using AI to solve fraud detection.  "The companies winning are the ones hiring to solve a business problem, not just to have an AI team," he says. Amid the hiring boom, expectations of what the role delivers are changing, with companies prioritising real‑world delivery over technical knowledge. Dr. Thomas Kelly, CEO and Co-Founder of AI healthcare startup Heidi, says the strongest candidates clearly explain their impact. "We look for concrete examples of what was built, why it mattered and the individual's role in delivering it. It also helps to draw clear links between past experience and what we do at Heidi, particularly around applied AI and healthcare," he says. According to Chipo Riva, Senior Consultant at recruitment agency Talenza, employers want engineers who can deploy AI in environments "where data is messy, stakeholders are sceptical and commercial outcomes matter more than technical elegance". "What truly differentiates candidates is the ability to translate business problems into AI solutions and explain trade-offs clearly to non-technical stakeholders," she says. "The market is saturated with people who can code. It's desperately short of people who can code and collaborate effectively across business functions." What does the rise of AI engineers say about how the workforce is changing? Share your thoughts in the comments below. See our full LinkedIn Jobs on the Rise list: https://lnkd.in/JOTR26Au By Brendan Wong #JobsOnTheRise

I recruit for Airbnb. Before that, I spent 18 years placing engineers into Google, Microsoft, and every company you've probably dreamed about. Some of them had the best careers of their lives. Some of them peaked the day they accepted the offer. The difference was never the company. Here's what I keep seeing: Engineer grinds 2 years. LeetCode. System design. Mock interviews every weekend. Cracks FAANG. Updates LinkedIn headline. Family is proud. Friends are impressed. WhatsApp group goes crazy. And then... they stop. Stop building their network. Stop documenting their impact. Stop thinking about what's next. Because they think they've "arrived." The FAANG brand is real. I'm not dismissing it. But here's what I see from inside the hiring room: A brand on your resume doesn't compound on its own. The engineers who thrive long-term? → They treat the offer as a starting point, not a finish line → They build internal visibility - not just good work, but known good work → They stay curious about the market even when they're comfortable → They're thinking Staff or Management from day one, not when the promo cycle surprises them The engineers who stagnate? They optimise for the offer letter. Then they optimise for comfort. One more thing nobody says out loud in India: Some of the strongest career trajectories I've seen in the last 3 years? Engineers who turned down FAANG for the right Series B in Bangalore. Who now have equity worth more than 3 years of FAANG salary. Scope that FAANG wouldn't have given them for another 6 years. And a title their batchmates at Google are still waiting for. The goal was never the company name. The goal was always the career. Don't confuse the two. What's a career move that surprised even you - where it turned out better than the "obvious" path? Joshua Talreja Views are my own. #techcareers #india #engineering #faang #careers

🚀 Which Job Roles Are in Highest Demand in Semiconductor Companies? 🧪 1. Process Engineers (Highest Demand in Manufacturing) What they do: Optimize each step of chip fabrication: lithography, etching, deposition, CMP, ion implantation. Why in demand: A modern fab has 1,500+ process steps. Every percentage gain in yield = millions saved. Analogy: Like chefs who fine-tune the recipe so the bakery (the fab) produces perfect cakes every time. 🔍 2. Equipment Engineers What they do: Maintain and optimize multi-million-dollar machines: EUV lithography Etchers Deposition tools Why in demand: Each EUV machine costs $200M+. Downtime = millions lost per hour. Analogy: They’re the F1 pit crew of the fab — equipment must run at perfect performance. ASML added 14,000 new engineers in the past few years, primarily equipment specialists. 🏭 3. Yield Engineers What they do: Identify defects, improve yield, reduce scrap. Why in demand: A single 300mm wafer can hold thousands of chips. 1% yield improvement = millions of dollars. 📐 4. Design Engineers (Chip Designers) Includes: RTL engineers Digital/Analog designers ASIC engineers SOC architects Memory designers Why in demand: AI, 5G, EVs, and cloud computing need new chip designs every year. Analogy: These are the architects designing skyscrapers (chips) before builders construct them. Example: NVIDIA and Apple hire hundreds of SoC and GPU design engineers annually for next-gen chips. 🧠 5. Verification Engineers (Critical in Chip Design) Role: Test the chip design thoroughly before fabrication. In demand because: Verification consumes 60–70% of total design time in large SOC projects. Analogy: They’re like test pilots ensuring the airplane is safe before passengers fly. 🔌 6. Test Engineers Role: Develop strategies to test finished chips: E-test Wafer-level test Final test Why in demand: Testing accounts for up to 25% of chip production cost. Example: Qualcomm and MediaTek employ massive test engineering teams for smartphone SOCs. 🔧 7. Packaging & Assembly Engineers (OSAT Roles) Includes: Advanced packaging 2.5D/3D integration TSV, chiplets, CoWoS Thermal management Why in demand: Packaging is now as important as transistor scaling. Analogy: Like building multi-storey buildings with tight plumbing/electrical systems stacked on top. Data: TSMC’s CoWoS capacity demand increased 3× in 2023–24 due to AI chips. 🌡️ 8. Materials Engineers & Chemists Role: Develop gases, photoresists, slurry, deposition materials, CMP chemicals. Why in demand: Every advanced node (5nm, 3nm, 2nm) needs new materials. Analogy: Just like Michelin-star chefs need specialty ingredients, fabs need ultra-pure electronic chemicals. Example: JSR, BASF, and Shin-Etsu actively hire material scientists for EUV photoresists. ~~~~~ If you are looking to invest in semiconductors and need expert insights, drop us a DM.