Recent AI/ML Placements

PE Limited, also known as Petex, a software company in Guildford, built its AI function from scratch through Digitalent: the AI Lead first, then the four people who would report to them.

A team build has a sequencing problem an individual hire does not. The AI Lead had to be right first, because everyone after them would be their hire and their responsibility. Then the appointed lead had a three-month notice period, so the other four searches ran before they had started, with the CEO as hiring manager and nobody yet in the building to define what good looked like. Everyone works five days a week from Guildford, which narrowed all five searches to people already nearby or genuinely willing to move.

Five hires, from the AI Lead through to the engineers and data scientists reporting to them. The company went from no in-house AI capability to a working function.

PE Limited, also known as Petex, a software company in Guildford, appointed its first AI Lead through Digitalent: a hands-on engineer senior enough to set the direction and build the first systems personally.

PE works five days a week from its Guildford office, so the shortlist had to be people already within reach of it or genuinely willing to move, which rules out most of a market that has got used to working from anywhere. The brief also needed a builder rather than a manager: there was no AI function to inherit, so the first hire had to be senior enough to set the direction and hands-on enough to write the first production code themselves. And because the person who would go on to lead the team had not been appointed yet, the CEO was the hiring manager throughout, which is a different conversation from the one you have with an engineering leader.

PE's first AI team was built around this appointment, every hire through us. The company went from no in-house AI capability to shipping AI products on top of its existing software suite, which landed well with customers across the oil and gas industry.

With the AI Lead appointed, PE Limited needed the engineer who would build alongside them, and for a while the only other AI/ML engineer in the business.

Outside the AI Lead this was the only AI/ML engineer in the business, so there was nobody to learn the ropes from and nowhere for a weak hire to hide. Technical depth was the entry requirement rather than the thing that decided it. What decided it was whether someone could hold their own with the wider engineering team and with the business, inside an established company where the AI function was brand new and still had to earn its place. Guildford, five days a week in the office, narrowed the pool again.

Responsibilities have grown steadily since. They lead on projects, help with hiring into the team, and have built up real domain knowledge in oil and gas, which in a business like this one is most of what separates a capable engineer from a genuinely useful one.

A data scientist who came through PE Limited’s AI team search and was appointed somewhere else entirely, because the business recognised the fit was stronger in another team.

This search did not end where it started, and that was the point. The candidate came through the process for the new AI team, and during it became clear that their skills and experience fitted a different team in the business better than the one we had been engaged on. Seeing that means knowing a client well enough to look past the role in front of you, and raising it rather than pushing a strong candidate at the brief you happen to be working.

Hired into a different team from the one they originally interviewed for. A year on they are working on high-profile projects, well embedded in the business, and building AI into the products of a market-leading engineering software company.

With the AI Lead and engineers in place, PE Limited added the first data scientist to its new AI team, working on AI inside the product rather than analytics alongside it.

Guildford, five days a week in the office, narrowed the pool the same way it did on every PE search. The harder constraint was domain. PE’s products sit in a specialist corner of the oil and gas industry and there is no pool of data scientists who already understand it, so this had to be someone capable of a serious amount of upskilling, while also being the first data scientist in a function that had not existed a year earlier.

The first data science hire into PE’s new AI function, working on AI built into the products rather than on reporting.

PA Media Group, the 150-year-old news and media group, hired software engineers who work AI-natively across its StreamAMG, Podium and Alamy brands, as part of moving an engineering organisation of more than 100 people to AI-accelerated ways of working.

The brief was not for AI specialists. It was for software engineers who already work AI-natively, using AI-accelerated engineering tooling as a normal part of how they build. That is a new enough category that there is no established pool to search, no title that reliably identifies it, and no conventional way to screen for it. A CV does not tell you whether someone has genuinely changed how they work or has simply used the tools once.

Engineers placed across two brands within the group, as part of a wider programme moving more than 100 software engineers towards working natively with AI.

A senior software engineer hired into Podium, part of PA Media Group, with no AI signals on the CV and no professional experience of the stack being built, who has since become the profile the client calibrates every later hire against.

On paper this was a C#/.NET backend engineer with nothing on the CV about AI at all. No LLMs, no agents, no tooling. Any keyword search would have discarded the application in seconds, and several probably did. The brief made it harder still: a TypeScript and Node platform being built from scratch, a hybrid pattern requiring regular time in the office, a budget below the market rate for the experience being asked for, and an explicit instruction to avoid engineers who over-engineer. That last one rules out a great many people who interview well.

Two things decided it. The technical assessment chose Server-Sent Events over WebSockets for a one-way data feed and justified it on protocol overhead rather than novelty, picked a framework on benchmarks and native type support, decoupled ingestion from delivery with an internal event bus, and set out what scaling it would actually require. Restraint, argued for. The second was a way of reviewing other people’s code by severity, sorting review comments into boulders, pebbles and dust so a senior engineer never becomes the bottleneck on somebody else’s preferences. Placed as a Senior Software Engineer and now the benchmark the client uses when describing who they want next. When a client starts saying "someone like that one" instead of restating the job spec, the calibration has landed.

PE Limited hired a senior software engineer into its AI team, not to build models but to connect them to a C# and C++ product suite that customers already depend on.

Not an AI engineer, and not hired to be one. PE’s core products are built in C# and C++, and an AI team producing models and prototypes is worth very little until somebody wires that work into the software customers actually use. That job needs deep conventional engineering in the languages the estate is written in, alongside enough understanding of what the AI team is building to be a useful counterpart rather than a downstream ticket-taker. It is a combination almost nobody advertises for, and Guildford, five days a week, narrowed it further.

Sits inside the AI function, connecting the models and AI products it builds to an existing suite of engineering software. The bridge between an AI team and a real product line, which is where a lot of AI programmes quietly stall.

StreamAMG, part of PA Media Group, hired a software engineer whose CV read junior and whose technical assessment was the most production-shaped, and best documented, we had reviewed for that role.

On experience alone this candidate read junior, and on a conventional sift would have been filtered out on years rather than assessed on work. The hiring manager’s own reaction after the first-stage interview was that the experience looked junior but the person did not. That gap is invisible on a CV and it is the whole reason this kind of hire gets missed.

The technical assessment settled it. A production-shaped submission rather than a toy one: interface-first abstractions, dependency injection, test coverage across every layer including a test that listeners were cleaned up on disconnect, and forty-plus atomic commits. The AI workflow documentation was the strongest we had seen on an assessment of that type, showing where the candidate had pushed back on the model rather than accepted it. Offer made five weeks after the brief opened.

Podium, part of PA Media Group, hired a software engineer with no professional TypeScript on the CV at all, on the strength of learning velocity and genuine AI fluency rather than stack tenure.

The specification asked for years of professional TypeScript, a hybrid pattern that ruled out remote-first candidates, and a commitment to an on-call rotation once the team settled. Each of those narrows a pool. Together they close it. The strongest person we found had built a career in PHP and Laravel and had none of the TypeScript tenure the brief asked for, which on a conventional read is where the conversation ends.

The technical assessment answered the stack question outright: a clean pivot into TypeScript, React and Node, full type safety with no escape hatches, test coverage across the suite, real-time updates over websockets and schema validation on every request. It came with a documented AI-native workflow, development logs showing where the model had been directed and where it had been overruled, and internally built Model Context Protocol servers written to make the work more accurate. Placed alongside a senior engineer into the same new team.