Learn the work behind Data, AI & Forward Deployed Engineering
Practical explanations, career decisions and reproducible workflows. Read the reasoning, inspect the evidence and follow the next skill into a real programme.
AI side projects while employed: a moonlighting and IP check
Before you build an AI side project while holding a full-time job, check four things: what your employment contract says about outside work and inventions, whether your project overlaps with your employer's business…
AI roles in banks, pharma and manufacturing: how the work differs
A data scientist or AI engineer in a bank, a pharma company and a manufacturer may share a job title, but the daily work differs in three ways: the data you touch (transactions and text in banks, clinical and regulatory…
AI product manager role: evaluation-led launch decisions
An AI product manager owns a product or feature whose behaviour depends on machine learning or large language models. Beyond the usual product work of understanding users and prioritising, the role requires deciding how…
AI job postings that are really data entry: how to decode them
Some postings titled "AI Analyst", "AI Associate" or "GenAI Operations" describe work that is mostly data entry: copying information between systems, tagging records against a fixed list, or checking model output by…
AI governance and AI risk roles: what the job involves day to day
AI governance and AI risk roles make sure an organisation knows which AI systems it runs, what could go wrong with each, and what evidence shows the risks are controlled. Day to day, that means reviewing proposals for…
AI governance questions to answer before your project is approved
Before an internal AI project is approved, expect four groups of questions: security (where data goes and who can access it), privacy (whose personal data is involved and on what basis), risk (what happens when the…
AI engineer job title in India: what the work usually means
In most Indian job postings today, "AI engineer" means a software engineer who builds product features on top of large language models and other pre-trained models: connecting them to company data and tools, controlling…
AI coding assistants and company policy: what engineers should check
Before you use an AI coding assistant on work or client code, check four things: whether the tool is approved for this codebase, what code and data it sends to the provider and under which account, what the policy says…
AI certifications in 2026: what you can verify before you pay
Before paying for an AI certification, verify five things on the issuer's own website: that the issuer is who you think it is, that the exam version you will study for is current, whether a retirement or replacement…
AI agent and tool-calling interview questions, with failure cases
AI agent interview questions test whether you can make a model that calls tools behave safely when things go wrong: when it passes malformed arguments, loops without progress, takes an action it should have asked about…
Agent frameworks to learn for AI jobs: start with the concepts
For employability, learn the concepts that every agent framework implements — the model-and-tool loop, tool schemas, state, error handling, human approval and evaluation — then learn one framework in depth, chosen from…
Advanced Excel interview questions on XLOOKUP, pivots and Power Query
Advanced Excel interviews move past "do you know VLOOKUP" to whether you can build a report that survives new data: dynamic lookups, pivot calculations that mean what the manager thinks they mean, and Power Query steps…
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