At a Glance
- Tasks: Build and shape AI systems that solve real business problems from the ground up.
- Company: Leading global investment firm with a commitment to AI innovation.
- Benefits: Autonomy, visibility, and the chance to influence AI strategy and architecture.
- Other info: Exceptional opportunity for ambitious engineers to build meaningful AI solutions.
- Why this job: Make a genuine impact in a greenfield environment with complex datasets.
- Qualifications: Proven experience in production AI systems and strong Python skills.
The predicted salary is between 80000 - 100000 £ per year.
A leading global investment business is looking to make its first dedicated AI Engineering hire — a rare opportunity to build and shape AI capability from day one inside a highly respected organisation managing tens of billions in assets. This is far beyond experimentation or internal demos. The successful candidate will design, build, and own production-grade AI systems that solve real business problems — from advanced RAG pipelines and agentic workflows to internal AI platforms used across the organisation.
This role offers genuine autonomy, visibility, and the chance to influence AI strategy, architecture, and engineering standards at an early stage.
Why this opportunity stands out:- Foundational AI hire with significant ownership and influence
- Greenfield environment with freedom to shape tooling, architecture, and best practices
- Direct exposure to complex, high-value datasets and workflows
- Strong long-term investment and commitment to AI from leadership
- Opportunity to build systems with immediate, measurable impact
- Build production-grade RAG pipelines across complex unstructured data
- Design and deploy multi-agent AI systems and orchestration frameworks
- Integrate LLMs across OpenAI, Anthropic, and open-source ecosystems
- Develop semantic search, vector database, and graph-based retrieval systems
- Own AI evaluations, observability, governance, and reliability
- Build internal AI products that enhance decision-making and operational efficiency
- Proven experience shipping production AI/LLM systems used by real users
- Strong end-to-end engineering capability — from architecture through deployment
- Deep Python and backend engineering experience
- Strong understanding of modern AI tooling, RAG, and agentic systems
- Comfortable operating in ambiguity and building from scratch
- Uses AI tooling aggressively, but critically and responsibly
- Cares about product quality, reliability, and business outcomes — not just models
This is an exceptional opportunity for an ambitious AI Engineer looking to build something meaningful at the frontier of applied AI — with the autonomy, backing, and technical scope to make a genuine impact.
Principal AI Engineer employer: Harrington Starr
Join a leading global investment business in London as a Principal AI Engineer, where you will have the unique opportunity to build and shape AI capabilities from the ground up. With a strong commitment to AI from leadership, this role offers significant ownership, autonomy, and the chance to work with complex datasets that drive real business impact. Experience a collaborative work culture that values innovation and provides ample opportunities for professional growth in a greenfield environment.
StudySmarter Expert Advice🤫
We think this is how you could land Principal AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI space, especially those already working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially any production-grade systems you've built. This is your chance to demonstrate your end-to-end engineering capability and make a lasting impression.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and backend engineering skills. Be ready to discuss your experience with modern AI tooling and how you've tackled real business problems using AI.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team and making an impact.
We think you need these skills to ace Principal AI Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Principal AI Engineer. Highlight your experience with production-grade AI systems and any relevant projects that showcase your end-to-end engineering capabilities. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about building AI capabilities from the ground up. Share specific examples of how you've tackled ambiguity and built impactful systems in the past. Let us know why you're the perfect fit for this greenfield opportunity!
Showcase Your Technical Skills:Don’t hold back on showcasing your technical prowess! Mention your deep Python experience, familiarity with modern AI tooling, and any hands-on work with RAG pipelines or multi-agent systems. We’re looking for someone who can hit the ground running, so make sure we see your skills front and centre.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy – just follow the prompts and let us see what you’ve got!
How to prepare for a job interview at Harrington Starr
✨Know Your AI Stuff
Make sure you brush up on your knowledge of production-grade AI systems, especially around RAG pipelines and multi-agent frameworks. Be ready to discuss your past experiences in detail, showcasing how you've tackled real business problems with AI.
✨Showcase Your Engineering Skills
Prepare to demonstrate your end-to-end engineering capabilities. Bring examples of your work that highlight your deep Python skills and backend engineering experience. This is your chance to show how you can design and deploy robust systems from scratch.
✨Understand the Business Impact
It's not just about the tech; it's about how it drives business outcomes. Be prepared to discuss how your AI solutions have enhanced decision-making or operational efficiency in previous roles. Show them you care about product quality and reliability.
✨Embrace Ambiguity
This role requires someone comfortable operating in uncertain environments. Think of examples where you've successfully navigated ambiguity and built something meaningful. Highlight your ability to shape tooling and architecture in a greenfield setting.
