AI Engineer - Platform/MLOps

AI Engineer - Platform/MLOps

Full-Time 36000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Design and operate scalable AI infrastructure, empowering teams to accelerate their AI/ML development.
  • Company: Join hyperexponential, a pioneering tech company transforming the insurance industry with AI.
  • Benefits: Enjoy a £5,000 training budget, 33 days holiday, private healthcare, and equity options.
  • Why this job: Make a real impact in a fast-paced environment while working with cutting-edge AI technology.
  • Qualifications: Experience in building production AI infrastructure and delivering self-service tools or APIs.
  • Other info: Collaborative culture with exceptional growth opportunities and a commitment to diversity.

The predicted salary is between 36000 - 60000 £ per year.

About hyperexponential (hx)

At hyperexponential, we’re building the AI-powered platform that enables the world’s most critical decisions in a $7 trillion industry. Our platform brings together data, AI, and human expertise to give insurers the fastest path from submission to decision - helping them move faster, act smarter, and take on more risk with confidence. Backed by a16z, Highland Europe, and Battery Ventures, we’re scaling globally - already trusted by nearly 50 of the world’s largest insurers.

About the hxAI team

At hx, AI is central to how we build software and make decisions across the company. The AI Platform team provides the platform that makes all of this possible. We design, build, and operate the shared systems that allow teams to train, deploy, evaluate, and monitor AI safely at scale.

What you’ll be doing:

  • Designing and operating scalable AI infrastructure for LLM inference, prompt management, and evaluation pipelines.
  • Building self‑service tools, SDKs, and APIs that empower product teams to move from prototype to production 30% faster.
  • Instrumenting production AI/ML workloads with standardised logging, tracing, and evaluation metrics.
  • Implementing intelligent routing, caching, and provider optimisation via the LLM gateway.
  • Driving adoption of shared platform services to replace bespoke solutions.
  • Championing developer experience by delivering comprehensive documentation and responsive support.

What you’ll need to have done:

  • Built and deployed production AI infrastructure that scaled to support enterprise‑grade reliability and observability.
  • Delivered self‑service tools or APIs that enabled multiple product teams to accelerate their AI/ML development cycles.
  • Implemented evaluation frameworks, A/B testing infrastructure, or monitoring solutions.
  • Led initiatives to reduce AI compute costs through optimisation strategies.
  • Successfully migrated teams from bespoke AI solutions to shared platform services.
  • Prioritised and improved developer experience through documentation, support, or workflow enhancements.

You’re unlikely to thrive here if:

  • You prefer working in silos rather than collaborating closely with product and engineering teams.
  • You are uncomfortable with rapid change or ambiguity in technical requirements.
  • You are not motivated to take ownership and drive measurable impact beyond your immediate tasks.

Compensation

At hx, we’re committed to salary transparency. We offer equity across all roles at hx, making it a significant component of total compensation.

Benefits:

  • £5,000 training and conference budget for individual and group development.
  • 25 days of holiday plus 8 bank holidays (33 days total).
  • Company pension scheme via Penfold.
  • Mental health support and therapy via Spectrum.life.
  • Individual wellbeing allowance via Juno.
  • Private healthcare insurance through AXA.
  • Income protection and Life Insurance.
  • Cycle to Work Scheme.

Additional perks:

  • Top‑spec equipment (laptop, screens, adjustable desks, etc.).
  • Regular remote and in‑person hackathons, lunch and learns, socials, and game nights.
  • Exceptional opportunities for personal development and growth.

Interview process:

  • Initial chat with our Talent team (30 minutes).
  • Manager Interview (60 minutes).
  • Technical Assessment (e.g., code review, system design) (120 minutes).
  • Values Interview with Senior Tech Leadership (60 minutes).

Our commitment to Diversity

hxers are at the centre of everything we build. We recognise there is always more to do, and we take responsibility for shaping a workplace that is not only diverse but genuinely inclusive.

Next steps

If this opportunity resonates with you, we encourage you to apply or share it with your connections!

AI Engineer - Platform/MLOps employer: Hyperexponential Ltd

At hyperexponential, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Our commitment to employee growth is evident through our generous training budget, comprehensive benefits, and a vibrant work environment that encourages creativity and teamwork. Join us in London, where you'll have the opportunity to make a meaningful impact in the insurance industry while working alongside talented peers who are as passionate about AI and technology as you are.
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Contact Detail:

Hyperexponential Ltd Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land AI Engineer - Platform/MLOps

✨Tip Number 1

Network like a pro! Reach out to people in the industry, especially those at hx. A friendly chat can open doors that applications alone can't. Use LinkedIn or even our careers page to connect with current employees.

✨Tip Number 2

Prepare for your interviews by understanding hx's mission and values. Show us how your skills align with our goals in AI and insurance. Tailor your examples to demonstrate your impact and ownership in previous roles.

✨Tip Number 3

Practice makes perfect! Mock interviews can help you get comfortable with technical assessments. Focus on explaining your thought process clearly, as we value collaboration and communication just as much as technical skills.

✨Tip Number 4

Don’t forget to follow up after your interviews! A quick thank-you note can leave a lasting impression. It shows your enthusiasm for the role and keeps you fresh in our minds as we make decisions.

We think you need these skills to ace AI Engineer - Platform/MLOps

AI Infrastructure Design
MLOps
API Development
Self-Service Tools
Data Pipelines
Observability
Model Evaluation
A/B Testing
Performance Monitoring
Cost Optimisation
Developer Experience Enhancement
Collaboration
Adaptability to Change
Technical Documentation

Some tips for your application 🫡

Tailor Your Application: Make sure to customise your CV and cover letter to highlight how your experience aligns with the AI Engineer role. We want to see how you can contribute to our mission of transforming the insurance industry with AI!

Showcase Your Projects: Include specific examples of projects you've worked on that demonstrate your skills in building scalable AI infrastructure or self-service tools. We love seeing real-world applications of your expertise!

Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so make sure your achievements and experiences shine through without unnecessary fluff.

Apply Through Our Website: We encourage you to submit your application directly through our careers page. This way, you’ll ensure it reaches the right people and gets the attention it deserves. Plus, it’s super easy!

How to prepare for a job interview at Hyperexponential Ltd

✨Know Your AI Stuff

Make sure you brush up on your knowledge of AI infrastructure, especially around LLM inference and evaluation pipelines. Be ready to discuss your past experiences in building scalable systems and how they relate to the role at hyperexponential.

✨Showcase Your Collaboration Skills

Since this role involves working closely with product and research teams, be prepared to share examples of how you've successfully collaborated in the past. Highlight any projects where teamwork led to faster development cycles or improved outcomes.

✨Prepare for Technical Challenges

Expect a technical assessment that may include code reviews or system design discussions. Brush up on your coding skills and be ready to explain your thought process clearly. Practice articulating your approach to problem-solving in a way that showcases your expertise.

✨Emphasise Developer Experience

The role prioritises improving developer experience, so think about how you've contributed to this in previous positions. Be ready to discuss any documentation, support, or workflow enhancements you've implemented that made a difference for your team.

AI Engineer - Platform/MLOps
Hyperexponential Ltd
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