Tech Skills in High Demand Right Now

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - ... [Reached length limit; full text: https://lnkd.in/geQBWz6s ]

Powering Your Career with Cloud-Native Skills In today's rapidly evolving tech landscape, cloud-native skills are not just nice-to-have – they're essential. 𝗪𝗵𝘆 𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲? Scalability: Easily adapt to changing demands Security: Built-in best practices for data protection Flexibility: Adapt resources to match exact needs, potentially optimizing costs Agility: Faster deployment and updates Top Cloud-Native Skills to Develop: 1. Containerization (Docker, Kubernetes)    - Package and run applications consistently across environments 2. Infrastructure as Code (Terraform, CloudFormation)    - Automate infrastructure provisioning and management 3. CI/CD Pipelines (Jenkins, GitLab CI, GitHub Actions)    - Streamline software delivery and deployment processes 4. Observability & Monitoring (Prometheus, Grafana)    - Gain insights into application performance and health 5. Cloud Security    - Implement best practices for securing cloud-native applications 6. Serverless Computing (AWS Lambda, Azure Functions)    - Build and run applications without managing servers 7. Microservices Architecture    - Design scalable and maintainable distributed systems 8. Cloud Databases (Amazon DynamoDB, Google Cloud Spanner)    - Leverage managed database services for scalability and performance Investing in these skills can open doors to exciting opportunities in cloud engineering, DevOps, and platform development roles. Which of these skills are you currently focusing on? Any others you'd add to the list?

I got fired twice because I had poor soft skills. Then, I became VP at Amazon, where my job was more than 80% based on soft skills. This was possible because I stopped being an outspoken, judgmental critic of other people and improved my soft skills. Here are 4 areas you can improve: Soft skills are one of the main things I discuss with my coaching clients, as they are often the barrier between being a competent manager and being ready to be a true executive. Technical skills are important, but soft skills are the deciding factor between executive candidates a lot more than technical skills are. Four “soft skill” areas in which we can constantly improve are: 1) Storytelling skills Jeff Bezos said, “You can have the best technology, you can have the best business model, but if the storytelling isn’t amazing, it won’t matter.” The same is true for you as a leader. You can have the best skills or best ideas, but if you can’t communicate through powerful storytelling, no one will pay attention. 2) Writing Writing is the foundation of clear communication and clear thinking. It is the main tool for demonstrating your thinking and influencing others. The way you write will impact your influence, and therefore will impact your opportunities to grow as a leader. 3) Executive Presence Executive presence is your ability to present as someone who should be taken seriously. This includes your ability to speak, to act under pressure, and to relate to your team informally, but it goes far beyond any individual skill. Improving executive presence requires consistently evaluating where we have space to grow in our image as leaders and then addressing it. 4) Public Speaking As a leader, public speaking is inevitable. In order the get the support you need to become an executive, you must inspire confidence in your abilities and ideas through the way you speak to large, important groups of people. No one wants to give more responsibility to someone who looks uncomfortable with the amount they already have. I am writing about these 4 areas because today’s newsletter is centered around how exactly to improve these soft skills. The newsletter comes from member questions in our Level Up Newsletter community, and I answer each of them at length. I'm joined in the newsletter by my good friend, Richard Hua, a world class expert in emotional intelligence (EQ). Rich created a program at Amazon that has taught EQ to more than 500,000 people! The 4 specific questions I answer are: 1. “How do I improve my storytelling skills?” 2. “What resources or tools would you recommend to get better in writing?” 3. “What are the top 3 ways to improve my executive presence?” 4. “I am uncomfortable talking in front of large crowds and unknown people, but as I move up, I need to do this more. How do I get comfortable with this?” See the newsletter here: https://lnkd.in/gg6JXqF4 How have you improved your soft skills?

If you're in tech, you're sitting on a goldmine right now. While everyone's debating AI job displacement, the engineering sector is quietly becoming the biggest AI beneficiary. The World Economic Forum projects 78 million net new jobs by 2030, and IT and Engineering is leading the charge. This shift is creating entirely new job categories that didn't exist two years ago. Here are five emerging growth areas for IT and Engineering: 1. AI-native product development → AI Product Managers who understand ML lifecycles and enterprise pain points. 2. AIOps infrastructure → MLOps engineers are moving companies from AI experiments to production. Every enterprise needs these skills. 3. AI cybersecurity → Red teamers for LLMs are literally paid to break AI systems.  4. Enterprise data infrastructure → Vector database engineers managing RAG pipelines are helping AI systems access the right information at the right time. 5. Vertical AI specializations → LegalTech AI specialists, FinTech AI analysts, HR tech AI specialists—domain expertise + AI fluency is the new superpower. The numbers back this up: $632 billion in AI spending (including applications, infrastructure, and IT services) by 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems. Bottom line: The engineers who adapt fastest will have the most opportunities. In my latest newsletter, I break down exactly how to transition into each of these roles, plus the specific tools and skills that matter most. What AI role are you most curious about? #AI #Engineering #IT #FutureOfWork

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

Building AI products that customers love may be the most important skill for Product Managers in 2025. But most PMs are just sprinkling AI on top… instead of baking it into the experience. Here’s how to build AI that’s actually used and actually drives business value (with a real example): — I've written a whole blueprint here (no paywall): https://lnkd.in/eDGmsvZ5 Here's the short version. — AI SPRINKLES: FLASHY BUT FORGOTTEN You know these features when you see them: → Attention-grabbing but superficial → Sit on top of existing features → Used occasionally, if at all But it’s only a matter of time before users realise the truth. So here are some signs you can look for early on: - Features hidden in sidebars - Require new user behaviors to start - High initial curiosity, but lower retention - Marketed with fancy badges and animations - Can be removed without affecting the core product These are the "look, we have AI too!" features. Users try them once but then forget. — AI CAKE: INVISIBLE BUT INDISPENSABLE The best AI products don’t demand the spotlight. Their goal is to give the user the best experience. Here’s what makes them different: → Deeply integrated into product DNA → Work invisibly in the background → Re-imagines core experience → Solves real problems And here’s what it looks like for the user in the real world: - Self-naming workflows that adapt to what you're doing - Dynamically generated content without prompting - Insights that appear exactly when you need it - Problems solved before users notice them So the magic lies not in showcasing intelligence. But embedding it where it matters most. — THE KEY SHIFTS TO MAKE To move from AI sprinkling to baking: 1. Start with user needs first, not technology (Don't just ask where AI fits, identify real friction points) 2. Make intelligence invisible rather than showcasing it (Great AI doesn't rely on neon signs, it solves problems) 3. Enhance existing workflows instead of creating new (Take the existing user experience from 'good' to 'great') — AN EXAMPLE Take Attio for example. They didn't obsess over the fanciest AI transcription. They cared about turning call data into valuable insights. That's the difference. Because users don't say "Wow, cool AI." They think "Great, this makes my job way easier." — What AI products do you like most? Repost to share with others. P.S. If you liked this, you'll love my newsletter: https://lnkd.in/gp2cCv8K

Right now, everyone is rushing to learn AI—deep learning, LLMs, and complex machine learning techniques. But most companies aren’t struggling with AI... They’re struggling with basic data management, analytics, and decision-making. Yet, many job seekers believe they need to master deep learning to land a data science role when the reality is much different. Before focusing on AI, it’s essential to develop strong data fundamentals: + SQL and Data Manipulation – Extracting, cleaning, and structuring data efficiently is critical. SQL remains one of the most in-demand skills in data science. + Business-Focused Data Analysis – Companies prioritize professionals who can use data to drive decisions, optimize processes, and create measurable impact. + Data Visualization and Communication – Insights have no value if they can’t be communicated effectively. Data storytelling is an underrated skill that influences decision-making. + Problem-Solving with Simple Models – Many business problems can be solved using logistic regression, decision trees, and forecasting methods rather than complex AI models. Many businesses lack structured data, clean pipelines, and the ability to make sense of the information they already have. Before implementing AI, they need: - Better customer segmentation rather than an AI-powered chatbot - Stronger demand forecasting instead of deep learning solutions - Clearer sales and operations insights before investing in predictive modeling - Organizations are looking for data-driven decision-making. The ability to translate raw data into business impact is far more valuable than knowing how to fine-tune a large language model. Most entry-level roles don’t require deep learning. The focus is on: // Understanding and working with real-world messy data // Solving business problems through analytical thinking // Presenting insights in a way that leads to action AI is only as good as the data that powers it. Strong data fundamentals will always be more valuable than chasing the latest AI trends. Those who focus on building these skills will position themselves for long-term success.

AI didn’t just change how we build -  it changed what we need to build. What once required months of work and a team of engineers now takes hours and a few prompts. The cost of building has collapsed. But here’s the real question:  Does anyone actually need what you're building? As AI democratizes speed and scale, the real differentiator isn't velocity -  it's clarity. Knowing what to build, when, and why. These are the skills product managers and product leaders need to double down on: 1.  Financial & Market Fluency Understand the levers your customers care about. What are they solving for 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸? How are macro shifts reshaping the problem space?     2. Discovery Mastery Dig deeper than feature requests. See the unspoken needs. Ask better questions and connect the dots others miss.     3. Hypothesis-Driven Mindset AI tools make testing faster and cheaper — use them. Explore bold bets without overcommitting. Ship learning, not just features.     4.  Strategic Prioritization Just because you 𝘤𝘢𝘯 build something doesn’t mean you should. Tie product bets to long-term outcomes. For platform PMs, that includes balancing internal vs. ecosystem value.     5. Relationship Building Talk to customers. Align with stakeholders. Influence across functions. Empathy and trust are still your sharpest tools.     6. Storytelling Your ability to shape a vision, influence decisions, and rally teams depends on how well you tell the story — especially in a world flooded with noise. In a recent mentoring session, someone asked me: “With AI evolving so fast, how do I stay relevant as a PM?” This is how. Use AI to accelerate execution -  but build your edge in the skills AI can’t replace. 👇 Which of these are you investing in this year? What else belongs on this list? #ProductLeadership #ProductStrategy #ProductManagement #AI #PlatformProducts #

I am a Senior Data Engineer at Amazon with more than 11+ years of experience. Here are 5 pieces of advice I would give to people in their 20s, who want to make a career in Big Data in 2025: ◄ Stop obsessing over fancy tools [ Master SQL first ] - Become fluent at writing complex joins, window functions, and optimizing queries. - Deeply understand ETL pipelines: know exactly how data moves, transforms, and lands in your warehouse. - Practice schema design by modeling real datasets (think e-commerce or user analytics data). ◄ Get hands-on with cloud, not just theory - Don't just pass AWS certification exams, build projects like a data pipeline from S3 to Redshift or an automated ETL workflow using AWS Glue. - Learn Kafka by setting up a simple real-time data streaming pipeline yourself. Set up an end-to-end analytics stack: ingest real-time data, process it with Airflow, Kafka, and visualize with QuickSight or Power BI. ◄ System Design is your secret weapon - Don't memorize patterns blindly, sketch systems like a Netflix-like pipeline, complete with partitioning and indexing choices. - Practice explaining your design to someone non-technical, if you can’t, redesign it simpler. - Understand real trade-offs like when to pick NoSQL (DynamoDB) vs SQL (Postgres) clearly, with real-world reasons (transaction speed vs consistency). ◄ Machine learning isn't optional anymore - Go beyond theory: integrate real ML models into your pipelines using something like Databricks or SageMaker. - Experiment with ML-based anomaly detection, build a basic fraud detection pipeline using real public datasets. - Know basics of Feature Engineering, prepare datasets used by data scientists, don’t wait for them to teach you. ◄ Soft skills will accelerate your career - Learn to clearly communicate business impact, not just tech specs. Don’t say "latency reduced," say “users see pages load 2x faster.” - Document like your future self depends on it, clearly explain your pipelines, edge cases, and design decisions. - Speak up early in meetings, your solutions won’t matter if no one understands them or knows you created them. – P.S. I'm Shubham - a senior data engineer at Amazon. Follow me for more insights on data engineering. Repost if you learned something new today!

The Next Big Skill in QA: Testing Custom AI Models and GenAI Apps A massive shift is happening in Quality Assurance—and it’s happening fast. Companies everywhere are hiring QA Engineers who can test custom AI models, GenAI applications, and Agentic AI systems. New tools like: • Promptfoo (benchmarking LLM outputs) • LangTest (robust evaluation of AI models) • And techniques like Red Teaming (stress-testing AI vulnerabilities) are becoming must-haves in the QA toolkit. Why is this important? Traditional QA focused on functionality, UI, and performance. AI QA focuses on: • Hallucination Detection (wrong, fabricated outputs) • Prompt Injection Attacks (hacking through prompts) • Bias, Ethics, and Safety Testing (critical for real-world deployment) ⸻ A few real-world bugs we’re now testing for: • GenAI chatbot refuses service during peak hours due to unexpected token limits. • Agentic AI planner gets stuck in infinite loops when task chaining goes slightly off course. • Custom LLM fine-tuned on internal data leaks confidential information under adversarial prompting. ⸻ New Methodologies Emerging: • Scenario Simulation Testing: Stress-test AI agents in chaotic or adversarial conditions. • Output Robustness Benchmarking: Use tools like Promptfoo to validate quality across models. • Automated Red Teaming Pipelines: Constantly probe AI with bad actors’ mindsets. • Bias & Ethics Regression Suites: Identify when fine-tuning introduces unintended prejudices. ⸻ Prediction: In the next 12-18 months, thousands of new QA roles will be created for AI Quality Engineering. Companies will need specialists who know both AI behavior and software testing fundamentals. The future QA engineer won’t just ask “Does the app work?” They’ll ask: “Is the AI reliable, safe, ethical, and aligned?” Are you ready for the AI QA Revolution? Let’s build the future together. #QA #GenAI #AgenticAI #QualityEngineering #Promptfoo #LangTest #RedTeaming #AIQA