Director of AI/ML Engineering

WeDo; Greater London; Hybrid; GBP 140,000 - 200,000; Full time

Job summary

WeDo is seeking a Director-level individual contributor to lead and shape how a major banking organisation builds with ML and AI. You will drive architectural decisions, balance research and production concerns, and influence multiple engineering teams to accelerate delivery of AI-enabled products.

This is a highly technical leadership role that stays hands-on, with responsibility for zero-to-one solutions, scalable ML systems, and practical evaluation of models in production.

Qualifications

• Background rooted in applied machine learning and building production ML systems.
• Ability to design and implement complex production ML architectures.
• Experience influencing technical direction across multiple teams.
• Comfort solving ambiguous problems from first principles and moving to implementation.

Skills

Applied ML expertise

Engineering excellence

Cross-team influence

Zero-to-one problem solving

LLMs / Generative AI

Job description

London | Hybrid – 3 days onsite | Permanent

An opportunity to shape how a major global banking organisation builds with machine learning and AI.

Wedo has partnered with a major global banking organisation that's investing significantly in its AI and machine learning capabilities.

They already have exceptional software engineers, established engineering teams and a growing pipeline of AI-enabled products in development.

We're looking for a distinguished ML engineering specialist who combines deep applied machine learning expertise, a strong data science background and exceptional software engineering ability.

Someone who's built sophisticated ML systems in production, operated at scale and understands the science behind them. Someone who can recognise problems before they become expensive mistakes.

This is a Director-level individual contributor position.

You're deeply technical. You consider code a first-class citizen and still love writing it. You're comfortable solving complex, ambiguous problems from zero to one, and you've developed the ability to influence how multiple engineering teams approach difficult technical challenges.

Your seniority has increased the complexity of the problems you can solve, not the distance between you and the code.

The problem being solved

Engineers across the business are developing AI-enabled products, experimenting with agentic workflows and introducing machine learning into increasingly complex problems.

But there's a difference between knowing how to integrate an LLM into an application and understanding how to design, build, evaluate and operate sophisticated machine learning systems at scale.

As the organisation moves further into applied AI, that difference becomes increasingly important.

Today, experienced software engineers are learning new ML frameworks and engineering approaches while delivering live projects.

They're making progress, but they're also encountering problems that someone with deeper applied ML experience would recognise immediately.

Architectural decisions need revisiting. Teams sometimes solve problems independently that others have already solved elsewhere. Progress can be slower than it needs to be.

We're looking for someone who can fundamentally improve how an entire engineering organisation approaches machine learning.

Someone who can challenge assumptions, identify opportunities others are missing and establish the technical foundations that allow multiple teams to move faster.

The experience we're looking for

There isn't one career path that produces this person.

You might currently be a Staff ML Engineer at a major technology company, a Principal Applied Scientist, a Distinguished Engineer specialising in ML, or a Director of Machine Learning who has deliberately remained close to the technology.

You may have started in quantitative development, data science, applied research or software engineering before moving deeper into machine learning.

Your title matters less than the work you've actually done.

But there are some characteristics we believe are essential.

1. Your background is rooted in applied machine learning

• You've spent a meaningful part of your career building machine learning systems, not simply integrating AI services into software applications.
• You understand the underlying principles of modelling, statistics, algorithms and evaluation.
• You've worked across different areas of ML, developing the judgement to know which approaches work, where they fail and why.
• We value experience across traditional machine learning as much as more recent developments in generative AI.

2. You're still an exceptional engineer

• You can design complex systems and implement them.
• You've built production software, understand modern engineering practices and are comfortable working through difficult technical problems in code.
• You don't need a team of engineers to turn every one of your designs into reality.
• You can build the foundations yourself, prove an approach works and help others scale it.

3. You've operated beyond a single team

• You've influenced technical direction across multiple engineering teams, ideally within a large, complex engineering organisation.
• You understand that solving a technical problem is only part of the challenge.
• You've developed the credibility to influence experienced engineers and senior stakeholders who don't report to you.
• You can introduce better engineering approaches and bring multiple teams with you.

4. You've built things from zero to one

• You've taken ambiguous, complex problems and worked through them from first principles.
• You can move between high-level architecture and low-level implementation without losing sight of the wider objective.
• You're comfortable making decisions where there isn't an established playbook.

5. You've kept pace with modern AI

• You've worked with, or developed a deep practical understanding of, LLMs, generative AI and agentic systems.
• You understand the opportunities they create, but you're equally comfortable challenging their limitations.
• You're interested in how modern AI changes engineering, rather than simply adopting whichever framework is currently attracting attention.

Who this role probably isn't for

We want to be clear about what we're looking for, because this is a particular type of opportunity.

If you're an excellent software engineer whose AI experience is primarily connecting LLM APIs, building RAG applications or integrating existing AI services, this probably isn't the right role.

Those skills are valuable, but we're looking for someone with deeper applied machine learning expertise.

If you've progressed into senior leadership and no longer want to write code, this probably isn't the right role either.

This is a technical leadership position, not a people management position.

And if your experience has been limited to owning a single product or small engineering team, the scale of influence expected here may be a significant step up.

We're looking for someone who combines all three:

• Deep applied machine learning knowledge.
• The engineering ability to build sophisticated production systems.
• The experience and credibility to influence technical direction across an organisation.

That combination is unusual. It's also why this opportunity exists.

Interested?

If you've built your career at the intersection of applied machine learning, software engineering and technical leadership, we'd be interested in having a conversation.

Particularly if you've reached a point where you're looking for a challenge that allows you to stay deeply technical while having a much broader impact.

You don't have to choose between writing code and influencing an organisation.

This opportunity gives you the chance to do both.

The organisation is being kept confidential at this stage. Further details will be shared directly with candidates whose experience aligns with the challenge.