Hi everyone - I’m Srinivas (https://www.linkedin.com/in/sraddepalli). I’m based in the UK and come from an IT infrastructure and cyber security background, with experience in SOC operations and cloud security across major industries. I’m now exploring Generative AI to see how it can enhance IT operations and security workflows. I’m available now and actively looking for a new role within the UK, so please do let me know if you hear of any opportunities where my skills could add value.

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What is AI Governance to you - and why 2026 is the year for it to be considered and implemented specially for enterprises

Here’s a step-by-step roadmap to become an AI Engineer — structured into clear stages you can follow from beginner to job-ready. This is based on the AI Engineer role definition from roadmap.sh (practical AI application work rather than pure research) combined with widely accepted AI career progression patterns. ——————————————— Stage 0 — Understand the Role (Orientation) Goal: Know what an AI Engineer does and how it fits in the AI ecosystem. - An AI Engineer uses pre-trained models and existing AI tools to create intelligent solutions and enhance user experiences — different from AI researchers or ML engineers focused on new model research. - Common tasks include integrating models into applications, optimizing AI workflows, and automating intelligent behavior. ——————————————— Stage 1 — Build Strong Foundations (0–3 Months) Goal: Establish the core prerequisites that every AI Engineer needs. 1. Programming Fundamentals (Python) Master Python, especially for data manipulation and scripting (NumPy, Pandas). Learn basics of Git and version control. Focus on writing clean, reusable code. 2. Mathematics for AI Linear algebra (vectors, matrices). Probability & statistics (distributions, hypothesis testing). Basic calculus (gradients, derivatives for optimization). 3. Computer Science Basics Data structures & algorithms (arrays, trees, hash maps). Object-oriented programming concepts. Basics of system design and software engineering best practices. ——————————————— Stage 2 — Intro to Machine Learning (3–6 Months) Goal: Understand how machines learn from data. 1. Supervised & Unsupervised Learning Linear/logistic regression, decision trees, clustering. Train/test splitting, evaluation metrics. 2. Practical ML Workflow Data preprocessing, feature engineering. Model training, validation, and tuning with Scikit-Learn. 3. Hands-On Projects Build simple projects to classify images or predict numerical values. ———————————————

Congratulations @Mucha Murapa @Srinivasa Raju A and @Neha Maurya for topping the leaderboard this week. As promised you will get free access to the webinar.