The career stories enterprise IT pros actually read.
Volume 343, Issue 1 · 9 articles from 2 issues
Nick Corcodilos tells a 64-year-old ex-agency owner to pitch a new business to contacts over lunch rather than hunt for a job.
A 64-year-old reader who built and sold a $137 million, 55-person media-buying agency asks Ask The Headhunter how to find part-time consulting work with smaller shops, and whether an agent could help.
Nick Corcodilos says there is no such agent and that the reader's business contacts are the best agents.
He advises taking the best of them to lunch, pitching a 'new kind' of media business for firms that can't afford senior talent full time and asking for advice rather than job leads, which can surface partners, funding, clients and even job offers.
Stack Overflow's 2026 survey shows freelance developers rising from 4% to 10% as AI lets one person do more, and job satisfaction is low.
Stack Overflow's 2026 developer survey of 30,903 respondents in 169 countries found the share working as a company of one rose from 4% in 2025 to 10%.
Stack Overflow attributes the shift to AI tools that let individuals handle work outside their primary expertise, creating full-stack freelancers and alternative arrangements after layoffs.
Only 22% of developers said they are happy in their current jobs, with a third unhappy and 45% complacent, signaling workforce changes IT leaders must factor into talent and sourcing strategies.
A practical breakdown of what ML, AI and LLM engineers actually build, to cut through confusing job titles.
Because job postings use AI Engineer, Applied AI Engineer and LLM Engineer interchangeably, the article compares the roles by what each actually ships.
ML engineers train and productionize models from data, such as fraud scores or forecasts, and spend most of their time on data quality, while AI engineers connect an existing model behind an API to products through prompts, retrieval, evaluation and monitoring.
LLM engineers are a narrower version focused on language models, adding fine-tuning of pretrained weights for specific use cases.
An engineering leader found AI tools let juniors ship 14 features yet build no mental models, and redesigned onboarding.
After six months of mandatory AI coding tools, Garima Agarwal's team had better velocity and cycle time, but junior engineers couldn't explain how the services they had shipped features against actually worked.
The gap surfaced when a junior engineer pulled into a race-condition incident had no idea where to start in a service he had changed two months earlier, because AI had removed the productive struggle that builds intuition.
Rather than restricting AI, the team spent six months redesigning onboarding to rebuild understanding, and the author describes what changed and what it cost.
A step-by-step roadmap for AI careers, from Python and data basics to generative AI, agents and MLOps.
Analytics Insight offers a learning roadmap for AI careers spanning engineering, data science, research, product and operations, starting with Python, SQL, data handling and statistics, then machine learning fundamentals such as supervised learning, feature engineering and model evaluation.
Next come deep learning with PyTorch or TensorFlow and practical generative AI skills including LLM APIs, embeddings, RAG and vector databases.
Finally, learners should add agentic patterns, evaluation, Docker, model serving and monitoring to become job-ready.
Falling computing enrollments in the U.S., UK and Australia signal a third tech talent drought that could last a decade.
A CIO.com column warns that the tech talent pipeline is shrinking on two fronts: fewer students choose computer and information science, and hiring managers expect weaker near-term demand for graduates.
U.S. CS and IS enrollment fell more than 8% in 2025 while overall undergraduate enrollment rose 1%, UK computing entrants dropped 9%, and Australian IT degree enrollment fell 21% heading into 2026.
The author notes the profession has weathered two earlier talent declines, each taking more than a decade to recover, and urges leaders to keep hiring and developing early-career talent.
Harvey Nash recruiters offer five tips for directors aiming for CIO, CTO and other tech C-suite roles.
Executive recruiters Kirsteen Bell and Peter Birch of Harvey Nash outline five ways to reach the tech C-suite, starting with building a track record of measurable results and expecting five to 10 years at director level.
They also advise being flexible about moving between large corporates and smaller firms or start-ups, and gaining hands-on AI experience, since boards now expect leaders who can apply AI responsibly at enterprise scale.
The final two are developing commercial and leadership skills such as communication and handling ambiguity, and building a personal brand through speaking and writing.
Chelsea Troy argues building engineering skill requires slowing down, even when AI tools speed up outcomes.
Summarizing Chelsea Troy's Craft conference talk, InfoQ explains her distinction between figuring things out and getting things done, arguing that AI and other tools accelerate outcomes but often do not build skill, because learning is inefficient by design and requires sitting with confusion and frustration.
Engineers still need to understand how the systems they modify work.
Troy points to spaced repetition, focused attention and sleep as the essentials of skill building, and suggests senior developers coach juniors by spreading learning over time.
Storytelling, stakeholder management and emotional intelligence top the soft skills that decide whether CIOs succeed.
Simon Ratcliffe of Freeman Clarke writes in TechTarget that technology may get someone appointed CIO but rarely determines their success, since modern CIOs operate where technology, finance, operations, risk and politics meet, making soft skills neither optional nor easy.
Key skills include storytelling that ties technical arguments to business outcomes, stakeholder management that understands what each group values and fears, and emotional intelligence to handle the personal impact of change.
Ratcliffe stresses these are learned mainly by practice, often by doing uncomfortable things in front of others.