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But the more conversations I have with engineering teams, the less they talk about AI… and the more they talk about something else entirely. Quiet rebuilds. Not new products Not big launches Not transformation programmes Just teams going back and fixing what’s already there Pipelines that have grown messy over time Data models that no one fully trusts anymore Dashboards that mean different things to different teams Workflows held together by a mix of legacy logic and good int

Over the last couple of years, AI has become a priority for almost every organisation. There are strategies, roadmaps and plenty of early-stage projects underway. But inside many teams, a familiar pattern is starting to emerge. Data engineers are being hired after things start to break. The Typical Sequence It usually starts with a clear objective. Improve reporting Introduce machine learning Explore generative AI use cases A proof of concept gets built quickly, often using e

Artificial intelligence is quickly becoming part of the recruitment process. From sourcing tools to CV screening and candidate matching, AI can scan thousands of profiles and surface people who appear to fit a role faster than any human recruiter could. On paper, it should make hiring far more efficient. But there is one problem AI can’t easily solve. Candidate ego. AI relies heavily on the information it reads. Job titles, keywords, skills listed on profiles and descriptions

Over the last 18 months AI has gone from an interesting capability to a board level priority. Almost every organisation now has some form of AI strategy, a pilot project or a roadmap. But inside many tech teams there is a quieter conversation happening. Fatigue. Not because engineers are against AI. Most are actually excited by it. The frustration comes from the gap between the expectation and the reality. Leadership conversations often start with the question “How do we impl

Everyone talks about AI. Very few talk about the people who make AI possible. Behind every successful AI initiative, analytics platform or reporting dashboard sits one critical role. The Data Engineer. AI Is Only As Good As The Data Beneath It You can invest in models, tools and consultants. But if your data is: Fragmented Poorly structured Inconsistent Slow to access You don’t have an AI problem. You have a data engineering problem. We’re seeing more businesses realise that

There’s a lot of noise around Generative AI right now. Tools are improving weekly. Job titles are changing. Entire functions are being redefined. But behind the headlines, something more practical is happening. Businesses are quietly trying to figure out how to hire the right people to build, deploy and govern GenAI properly. And that’s where the real challenge sits. The Talent Gap Is Not Just About Engineers When people think about GenAI hiring, they immediately think of mac

Artificial intelligence is everywhere. Every company says it is “leveraging AI”. Every strategy deck mentions machine learning, automation or generative tools. But when you look behind the scenes, most organisations are struggling with one thing. Hiring the right people. The AI talent market is noisy. Job titles mean different things at different companies. A “Machine Learning Engineer” in one business is building production pipelines. In another, they are cleaning datasets.

Over the past 18 months, AI hiring across the UK has moved through a clear shift. The early wave was driven by hype. Companies wanted “AI talent” without always being clear what that meant. There was a rush to hire data scientists, ML engineers and anyone with LLM experience on their CV. Now the market feels more grounded. Organisations are still investing in AI, but the conversation has changed. It’s less about experimentation and more about delivery. Here’s what we’re seein

Generative AI has moved fast. Large language models are now widely available, increasingly commoditised, and easy to experiment with. Yet many organisations are still struggling to get real value into production. The reason is no longer the model. It’s the data. The most effective GenAI systems today are built on three pillars: strong data engineering foundations, retrieval-augmented generation (RAG), and clear production discipline. When these are missing, even the best mode

AI is everywhere in business right now, but it only works if the data behind it is ready. A lot of AI projects stall or fail because the data is messy, incomplete, or poorly structured. That’s where data engineers come in. They make sure the data is clean, reliable, and accessible so AI can actually deliver value. Here are five data engineering skills that every company looking to adopt AI should prioritise. 1. Data Warehousing Data warehouses like Snowflake, BigQuery, and Re

AI initiatives don’t fail because of models. They fail because the data isn’t ready. Across the UK and Europe, organisations are investing heavily in AI, machine learning, and GenAI yet many struggle to move beyond pilots. The common blocker is fragmented pipelines, unreliable datasets, and a lack of experienced data engineering ownership. At Raice AI Recruitment, we help companies hire high‑quality data engineers, contract or permanent who clean, structure, and scale data so

At Raice, we help organisations hire outstanding Generative AI researchers, Research Scientists, and PhD‑level AI talent across the UK and Europe. We are a specialist recruitment agency focused on GenAI, foundational models, and advanced AI research roles, with a strong understanding of the academic and commercial AI landscape. If you’re finding it difficult to hire researchers who can translate cutting‑edge theory into real‑world impact, Raice is here to help. Why GenAI & Ph

Artificial Intelligence is now embedded on both sides of the recruitment process. Candidates are using AI to write CVs at scale, while employers are relying on AI-powered Applicant Tracking Systems (ATS) to filter, rank, and reject applications before a human ever sees them. On paper, this sounds efficient. In reality, it’s creating a quiet arms race and not necessarily producing better hires. At Raice.AI Recruitment, we’re seeing the impact first-hand. The Rise of the AI-Wr

Raice.AI - January 2026 2025: From Experimentation to Accountability If 2024 was defined by hesitation and budget scrutiny, 2025 became the year UK organisations grew up around AI. Across financial services, retail, energy, life sciences and the public sector, the conversation shifted decisively. Artificial intelligence was no longer treated as a future bet or a boardroom buzzword but it also lost its “silver bullet” status. Leaders moved beyond pilots and prototypes, focusi

Building an AI team from scratch can feel like navigating a maze. There are countless roles, technologies, and approaches but not every hire matters equally in the early stages. At Raice AI Recruitment , we help companies prioritize the right talent to turn AI strategies into reality. Here are the first five hires that truly matter when building an AI team in 2026. 1. AI/ML Engineer Your first hire should be someone who can turn prototypes into production-ready systems . Why

In the evolving AI landscape of 2026, companies are increasingly hiring for roles that sound similar but are fundamentally different: Data Science and AI Engineering . Misunderstanding these distinctions can lead to misaligned teams, stalled projects, and wasted recruitment budgets. At Raice AI Recruitment , we guide organisations in building high-performing AI teams. One of the first steps is helping leaders clearly differentiate between these two critical disciplines. Data

Over the past few years, AI strategy decks have become a staple in boardrooms. Vision statements, roadmaps, maturity models, and vendor shortlists fill slide after slide. The language is confident. The ambition is bold. And yet, many of these AI strategies never leave the PowerPoint. At Raice AI Recruitment , we see the same pattern repeatedly: companies invest heavily in defining what they want to do with AI, but fail to invest in who will actually make it happen. Without th

Generative AI has moved far beyond experimentation. By 2026, GenAI is no longer a niche capability owned by a handful of research teams it is embedded into product development, operations, marketing, customer support, and decision-making at scale. Yet hiring for GenAI talent remains one of the most misunderstood challenges facing companies today. Many organisations still chase buzzwords, academic credentials, or tool-specific experience, only to discover that these hires stru

Pressure to “do something with AI.” Yet despite unprecedented investment, most AI projects still fail before they ever reach production . At Raice, we see this pattern repeatedly. The problem isn’t lack of ambition or intelligence. It’s a disconnect between experimentation and execution. Here’s why most AI projects stall and what successful teams do differently. 1. AI Projects Start as Experiments, Not Products Many AI initiatives begin as proof-of-concepts (POCs) built by in

At Raice, we believe in activating senior-led networks that deliver real outcomes in AI and tech. That’s why it was a pleasure to connect with Dr. Simant Prakoonwit, professor at Bournemouth University and fellow innovator whose recent project was selected as one of Innovate UK’s top success stories. His work, Protecting image-sharing users from inappropriate content with AI, tackles a critical challenge in online safety using intelligent filtering and classification. It’s no