Technical Skill Development

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

I wish someone had shown me this pyramid on Day 1 of my IT audit career. Would've saved me 6 months of confusion. When I started, I jumped straight to controls. Access reviews. Change management. Backup testing. I was checking boxes. But I had no idea WHY those controls mattered. No one told me to start at the top of the pyramid. The Business. What does this company actually do? How do they make money? What goals are they chasing? Without understanding that, every control I tested felt random. Then one day, my manager asked me: "Chinmay, why this IT Application is in scope for our audit?" I froze. Because I was testing controls in isolation. I never connected controls to IT apps and IT apps to the business process. Great auditors don't start at the bottom of the pyramid. They start at the top. You can't test what you don't understand. This framework changed everything for me. Understand the business → What goals drive this company? Map the core processes → What processes support those goals? Identify the applications → What systems enable those processes? Evaluate IT risks → What can go wrong in those systems? Test the controls → What mitigates those risks? Top to bottom. Always. If you're confused about where to start, save this infographic. Print it. Keep it at your desk. Because the biggest mistake I made wasn't bad testing. It was testing without context. Learn IT audit the way it's actually done. Because clarity is the difference between doing audit and understanding it. Tag someone who needs to see this framework. #itaudit #audit #risk #compliance #internalaudit #cisa #isaca

To prepare for technical interviews at FAANG (Google, Apple, Microsoft, Amazon, and Meta), here's strategy: To prepare for technical interviews, focus on solving coding problems regularly. 1. Practice Coding Every Day:   - Try solving at least one medium or two easy-level coding questions daily.   - Do it on your own without help, but if you're stuck for over an hour, look for hints or solutions.   - Make notes of what you missed while solving and revise them often. 2. Focus on Concepts:   - Spend time understanding the concepts behind each problem you solve.   - Revise your notes and practice problems regularly to strengthen your understanding. 3. System and Design Studies:   - Aim to prepare at least one system and one object-oriented design case study each week. 4. Stay Consistent:   - Consistency is key. Stick to your daily coding practice routine.   - Use the Pomodoro Technique: plan 25 minutes of focused preparation followed by a 5-minute break, and repeat. 5. Include Behavioral Interviews:   - Don't overlook behavioral interviews. Give them equal importance in your preparation. For effective use of LeetCode: 1. Quality Over Quantity:   - Focus on solving quality problems rather than just solving many.   - Follow a roadmap of quality problems, like the 100 Days to GAMAM plan. 2. Use Curated Lists:   - Solve LeetCode's curated list of top interview questions, including the top 100 liked questions. 3. Practice Weak Areas:   - Identify your weak areas and practice questions specifically in those topics.   - Sort problems by "Acceptance" after choosing a difficulty level for better chances of success. 4. Gradual Progression:   - If you're a beginner, start with easy-level problems and gradually move to medium and hard levels.   - Aim to solve a target number of problems at each level. 5. Utilize Resources:   - Check out multiple solutions to problems and understand their time and space complexities.   - Take notes on missed concepts and revise them regularly. 6. Challenge Yourself:   - Once you're comfortable with practice, try daily challenges and participate in contests.   - Track your progress and consistency using LeetCode's features, like session management and submission graphs. LeetCode Practice:   - Solve LeetCode problems daily for 1-2 hours.   - Focus on quality over quantity.   - Start with easy problems if you're a beginner.   - Practice topics where you feel weak.   - Check out multiple solutions for each problem.   - Aim for a balanced number of easy, medium, and hard problems. Problem Solving Techniques:   - Don't spend more than 45-60 minutes on a problem.   - If stuck, check hints or solutions, but try to understand them fully.   - Take notes on missed concepts and solutions.   - Revise problems frequently, following a schedule based on Ebbinghaus's Forgetting Curve. consistent practice, understanding concepts, and targeted preparation will help you ace your technical interviews! Follow Vikram Gaur #faang

"💡 What is internal audit? Internal audit is an organizational function that evaluates the adequacy and effectiveness of a company’s risk management, control, and governance processes. It is organizationally independent from senior management and reports directly to the board of directors. 🏛️ The role of internal audit in corporate governance In the Three Lines Model, which is a popular risk governance framework (https://lnkd.in/e8dWA8_D), internal audit serves as the third line and is responsible for providing independent assurance to the board of directors. But internal audit is not the only assurance provider. In many companies, the board also gets reports from compliance, external auditors, etc. To avoid blind spots and “assurance fatigue”, the different assurance activities need to be coordinated (e.g. by using the same terms, taxonomies, and threat models). Internal audit is often responsible for ensuring this coordination. ✅ Why frontier AI developers need an internal audit function Internal audit can identify ineffective or inadequate risk management practices. This is important because, without a deliberate attempt to identify flawed practices, some of them will likely remain unnoticed. For example, developers' model evaluations might be inaccurate or unreliable (see https://lnkd.in/embgeirH) or their information security might be inadequate (see https://lnkd.in/d9MTCgKx). Internal audit can also ensure that the board has a more accurate understanding of the current level of risk and the adequacy of risk management practices. For example, internal audit could verify if the company actually complies with its AI safety framework (see https://lnkd.in/e2ZnMyYT). ❌ Limitations But frontier AI developers should also be aware of key limitations: Internal audit adds friction, it can be captured by senior management, and the benefits depend on the ability of individuals to identify ineffective practices. In light of rapid progress in AI research and development, frontier AI developers need to strengthen their risk governance. Instead of reinventing the wheel, they should follow existing best practices. Although this might not be sufficient, they should not skip this obvious first step." Paper (and summary) are from Jonas Schuett and the Centre for the Governance of AI (GovAI).

LinkedIn is transforming its data tech stack with Apache Iceberg and open data formats—here's why that matters for all of us grappling with big data. By adopting Iceberg, LinkedIn enhances data management at petabyte scale, enabling better versioning, schema evolution, and performance. If you're facing challenges in handling massive datasets, LinkedIn's approach to leveraging open data solutions like Iceberg could be the game-changer you need. Read about OpenHouse, the management plane they use for Iceberg tables: https://lnkd.in/exQV__Pq Read more about LI's data infra here: https://lnkd.in/emwkj9GZ

𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗖𝗿𝗶𝘀𝗶𝘀: 𝗪𝗵𝘆 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗔𝗿𝗲 𝗙𝗮𝗶𝗹𝗶𝗻𝗴 Change is no longer a ripple; it’s a tsunami. And yet, most organisations are trying to tackle it with outdated tools and approaches. 💥 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵? Our way of “doing change” is failing. Over two decades as Head of People, Culture & Change, I’ve witnessed this firsthand. I’ve seen well-intentioned efforts unravel because we’re relying on the wrong playbook. We treat organisations like machines, where we “push” for change and “fix” problems. But in reality, organisations are complex human ecosystems. And these ecosystems require a completely different approach. Here are 7 reasons 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗶𝗻 𝗮 𝘀𝘁𝗮𝘁𝗲 𝗼𝗳 𝗰𝗿𝗶𝘀𝗶𝘀: 1️⃣ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴: We look for quick fixes instead of recognising the complexity of human systems.
 2️⃣ 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗚𝗿𝗼𝘂𝗽 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: We rely on IQ and EQ but neglect GQ—Group Intelligence—(sitting at the individual collective levels) which is the key to successfully intervening in adaptive ecosystems. 3️⃣ “𝗗𝗼𝗻𝗲 𝘁𝗼” 𝗖𝗵𝗮𝗻𝗴𝗲: Change is implemented to people, rather than with or by them. 4️⃣ 𝗢𝘃𝗲𝗿-𝗿𝗲𝗹𝗶𝗮𝗻𝗰𝗲 𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Thinking that software, training, or new processes alone will drive transformation. 5️⃣ 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗠𝗶𝗻𝗱𝘀𝗲𝘁: Believing upskilling is enough, without rewiring underlying patterns in the system. 6️⃣ 𝗟𝗶𝗻𝗲𝗮𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀: Focusing on step-by-step plans rather than embracing the emergent, nonlinear nature of transformation.
 7️⃣ 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗙𝗼𝗰𝘂𝘀 𝗢𝗻𝗹𝘆: Targeting individual behaviors (the “what”) while ignoring how the system operates (the “how”). The truth is, meaningful change can’t be forced. It must be emerged. Here’s what needs to change in Change Management: * 𝗙𝗿𝗼𝗺 𝗮 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗻𝘀 𝘁𝗼 𝗮𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗟𝗲𝗻𝘀. * 𝗙𝗿𝗼𝗺 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗙𝗶𝘅𝗲𝘀 𝘁𝗼 𝗦𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝗜𝗻𝘁𝗲𝗿𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀. * 𝗙𝗿𝗼𝗺 𝗟𝗶𝗻𝗲𝗮𝗿 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝘁𝗼 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝘁 𝗗𝗲𝘀𝗶𝗴𝗻. * 𝗙𝗿𝗼𝗺 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗖𝗵𝗮𝗻𝗴𝗲 𝘁𝗼 𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗚𝗿𝗼𝘂𝗽 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Change Management needs an overhaul. It’s time to align it with the complexity we face in today’s world. If we’re serious about building organisations that deliver, grow, and adapt, we need to move beyond the old ways. It’s time to embrace a systemic lens and build Group Intelligence. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗜𝘁’𝘀 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗰𝗵𝗮𝗻𝗴𝗲. Do you agree or disagree? 📘 Want to learn more? Discover the next horizon for Change Management in my book, 𝗧𝗵𝗲 𝗛𝗶𝘃𝗲 𝗠𝗶𝗻𝗱 𝗮𝘁 𝗪𝗼𝗿𝗸 (available on Amazon). #changemanagement

If your goal is to truly learn coding, consider turning off tools like GitHub Copilot—at least in the beginning. Yes, you read it right. Real learning happens when your mind is fully engaged in problem-solving. When you struggle with syntax, logic, and debugging, your brain opens up, it stretches and forces you to think. That mental effort is good, i have experienced it myself. it’s the process of learning. When Copilot is constantly suggesting solutions, ww immediately want to accept code without fully understanding how or why it works. don't you think? Over time, this can weaken your ability to think critically, design logic independently, and troubleshoot issues effectively. This is especially important for testers or beginners who are transitioning into coding or experienced people who wants to learn automation testing. At this stage, building strong fundamentals—understanding control flow, edge cases, and debugging techniques—is more important. AI tools like Copilot are powerful, we all know but they should be used ( once you know coding concepts) First, develop the habit of: -writing code by your hands,full line of code -Breaking down problems on your own -Writing logic step by step -debugging without hints -Understanding errors Once you build that foundation, AI can become a helpful assistant for sure, I believe. Use AI to enhance your skills—not to replace your thinking. don't be fully dependent on such tools. Are you usinggithub co-pilot autosuggestions blindly ? Or, do you first analyse the code before using it? lets discuss. #LearnToCode #DeveloperMindset #ProblemSolving #SoftwareTesting #Upskilling #GitHubCopilot #SDET

If you are preparing for AI / ML interviews, this is a Roadmap to prepare for GenAI System Design rounds. Do not neglect this, as maximum rejections come from this round. [1] Understand the Core Use Cases • Chatbots vs. RAG • Document summarization at scale • Multi-modal inputs (text, images, speech) • Streaming vs. batch processing for LLM tasks • Personalization in LLM outputs [2] Know the GenAI Building Blocks • LLM APIs (OpenAI, Anthropic, Gemini) • Vector databases (Pinecone, Weaviate, Chroma, Faiss) • LangChain, LlamaIndex, semantic caches • Tokenization and chunking strategies for long documents • Fine-tuning vs. prompt engineering • RAG architectures: how to wire everything together [3] Think Like an Architect When the interviewer asks: “Design a GenAI-powered search for legal documents”, approach it like this: - Data Ingestion • Doc formats? PDF? Audio? • Chunking strategy for embeddings - Embedding & Storage • Which model for embeddings? • Which vector store and why? - Query Flow • User query flow → retriever → reranker → LLM • Prompt templates and context window considerations - System Components • Async pipelines? • Caching strategies? • Handling model failures - Scale & Cost • Estimating token usage • Deploying open-source vs. paid APIs - Safety & Compliance • Data privacy concerns • Deploy guardrail to LLM outputs [4] Practice Whiteboarding GenAI Components Don’t just practice generic system design. Sketch diagrams for: • RAG pipelines • Multi-LLM orchestration • Hybrid retrieval (sparse + dense search) • Load balancing GenAI calls across providers • Using semantic caches to cut costs [5] Brush Up on Eval Metrics • Token costs and budget projections • Retrieval precision/recall (for RAG) • Quality evaluation for generated outputs (BLEU, ROUGE, human evals) I have written detailed articles about end-to-end RAG architecture and LLM Fine-tuning techniques — two very important topics for GenAI interview. [Link in comment]

The recent inadvertent exposure of classified U.S. military plans by top defense and intelligence leaders serves as a stark reminder that even the most capable cybersecurity tools and well-defined policies can be rendered meaningless if ignored or misused. In this case, senior leaders relied on the Signal messaging app to communicate sensitive data but unintentionally exposed critical information to unauthorized parties. The leaked details—time-sensitive plans for a military operation—could have not only placed personnel in greater danger but also undermined the mission by alerting adversaries to an imminent attack. While #Signal is a widely respected, consumer-grade, end-to-end encrypted communication tool, it does not provide the same level of security as classified government systems. National security organizations typically utilize Sensitive Compartmented Information Facilities (SCIFs) to safeguard classified data from leaks and eavesdropping. However, SCIFs and other highly-secure methods are not as convenient as less secure alternatives—such as personal smartphones. In this instance, Signal's encryption was not the issue; rather, the exposure occurred when an unauthorized individual was mistakenly added to the chat. This human error resulted in sensitive information being disclosed to a reporter. Lessons Learned: This incident highlights critical cybersecurity challenges that extend beyond the military and apply to organizations everywhere: 1.     Human behavior can undermine even the most robust security technologies. 2.     Convenience often conflicts with secure communication practices. 3.     Untrained personnel—or those who disregard security protocols—pose a persistent risk. 4.     Even with clear policies and secure tools, some individuals will attempt to bypass compliance. 5.     When senior leaders ignore security policies, they set a dangerous precedent for the entire organization. Best Practices for Organizations: To mitigate these risks, organizations should adopt the following best practices: 1.     Educate leaders on security risks, policies, and consequences, empowering them to lead by example. 2.     Ensure policies align with the organization’s evolving risk tolerance. 3.     Reduce compliance friction by making secure behaviors as convenient as possible. 4.     Recognize that even the strongest tools can be compromised by user mistakes. 5.     Anticipate that adversaries will exploit behavioral, process, and technical vulnerabilities—never underestimate their persistence to exploit an opportunity. #Cybersecurity is only as strong as the people who enforce and follow it. Ignoring best practices or prioritizing convenience over security will inevitably lead to information exposures. Organizations must instill a culture of cybersecurity vigilance, starting at the top, to ensure sensitive information remains protected. #Datasecurity #SCIF #infosec

🤔 Why does it feel like I’m stuck after watching hours of coding tutorials? Here's the hard truth: Watching someone code is like watching someone swim. You'll never learn to float by sitting on the beach. 🧠 You don’t become a better programmer by watching. You become one by doing. → If you’re learning web development, are you building websites from scratch? → If you’re learning data science, are you playing with datasets? → If you’re learning software engineering, are you coding small tools? → If you’re learning the fundamentals, are you coding basic challenges? Not sure where to start? Here are some great platforms to find challenges for any programming path:  👩🏾💻LeetCode - For algorithm and coding challenges. https://leetcode.com/ 👩🏾💻 HackerRank - Solve problems and build domain skills. https://lnkd.in/es9Qb3Gc 👩🏾💻 freeCodeCamp - Build projects while learning. https://lnkd.in/euXPmkfx 👩🏾💻Frontend Mentor - Real-world web development challenges https://lnkd.in/eFH9qud6 👩🏾💻 Kaggle - Explore data science competitions. https://www.kaggle.com/ 👩🏾💻 Exercism - Great for language-specific practice https://exercism.org/ 👩🏾💻 Codewars - Fun, gamified learning. https://www.codewars.com/ 👩🏾💻 Edabit - Short, fun coding challenges. https://edabit.com/ Remember: Active learning is more effective than passive learning. A single hour of writing code teaches more than 10 hours of watching tutorials. Tackle challenges, no matter how small. 𝗖𝗼𝗱𝗲. 𝗠𝗮𝗸𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀. 𝗙𝗶𝘅 𝘁𝗵𝗲𝗺. 𝗥𝗲𝗽𝗲𝗮𝘁. What small project will you start coding today? 💻 What other coding platforms will you recommend? #Programming #Tech #Growth #LearnWithSofiat