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- Tasks: Build AI tools to revolutionise finance and streamline investment processes.
- Company: Leading growth equity firm with a focus on innovation and collaboration.
- Benefits: Highly competitive salary, performance bonuses, and hybrid work model.
- Other info: Fast-paced environment with opportunities for personal and professional growth.
- Why this job: Shape the future of AI in finance and make a real impact.
- Qualifications: 5+ years in software or AI engineering, strong Python skills required.
The predicted salary is between 80000 - 100000 £ per year.
We are looking for a forward deployed AI Engineer to join a leading growth equity firm - an elite, hands-on individual contributor who will play a foundational role in shaping how AI is used across the investment lifecycle.
The successful applicant will have responsibility for E2E delivery (including all development and implementation), and the chance to build from a blank slate, shaping the firm's AI capability, including sourcing, research, diligence, and workflows. There are no plans for the AI Engineer to build a team.
You will be a fast-tracker and have worked in high-context environments where engineers work directly with end users to deliver real-world AI systems.
The role:
- Build internal AI-powered tools, systems and agents to streamline sourcing, research, diligence, and portfolio monitoring
- Design and deploy end-to-end LLM applications including research copilots, RAG systems, workflow automation, data enrichment, and deal intelligence tooling
- Ingest, clean, and analyse external datasets through scripts, pipelines, and lightweight ML components
- Integrate third-party APIs, datasets, scraping frameworks, and internal systems such as CRM and deal flow platforms
- Rapidly prototype MVPs, validate value with the investment team, and harden the most impactful tools into reliable internal products
- Build simple internal interfaces using FastAPI, Streamlit, or Next.js to enable intuitive adoption
- Implement evaluation, observability, and governance to ensure accuracy, reliability, security, and responsible use of AI systems
- Guide and coach the investment team on the latest tools, processes
Skills and Experience:
- 5+ years in software engineering, applied AI engineering, ML engineering, or forward deployed roles
- Strong Python
- Experience building and deploying LLM applications (OpenAI, Anthropic, Gemini, etc.)
- Deep familiarity with RAG methodologies, embeddings, and vector databases (Pinecone, Weaviate, pgvector)
- Robust API integration experience, including working with third-party datasets, web APIs, or scrapers
- A product mindset, enjoys crafting scrappy prototypes that evolve into polished tools
- Strong communication skills and the ability to collaborate directly with non-technical business users
- Exposure to finance, enterprise data tooling, or B2B SaaS analytics is a bonus
AI Engineer employer: Richard Wheeler Associates
Contact Detail:
Richard Wheeler Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the finance and AI sectors on LinkedIn. Join relevant groups, attend meetups, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those that relate to finance. Whether it’s a GitHub repo or a personal website, make sure potential employers can see what you can do. We love seeing real-world applications of your work!
✨Tip Number 3
Prepare for those interviews! Research the company and its AI initiatives thoroughly. Be ready to discuss how your experience aligns with their needs, especially around E2E delivery and building AI tools. Practice common interview questions and have your own questions ready to show your interest.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who want to shape the future of AI in finance. Don’t miss out on the chance to join us!
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI Engineer role. Highlight your experience with Python, LLM applications, and any relevant projects that showcase your skills in building and deploying AI systems. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with our needs. Be sure to mention your experience in high-context environments and your ability to work directly with end users.
Showcase Your Projects: If you've worked on any cool AI projects, don’t hold back! Include links or descriptions of your work, especially if it involves LLM applications or API integrations. We love seeing real-world examples of your skills in action.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and we’ll be able to review your application more efficiently. Plus, it shows you’re serious about joining us at StudySmarter!
How to prepare for a job interview at Richard Wheeler Associates
✨Know Your AI Stuff
Make sure you brush up on your knowledge of AI applications, especially in finance. Be ready to discuss your experience with LLM applications and how you've integrated APIs in past projects. This role is all about practical application, so be prepared to share specific examples.
✨Showcase Your Prototyping Skills
Since the job involves building MVPs and evolving them into polished tools, come armed with examples of your previous prototypes. Talk about the challenges you faced, how you validated their value, and what impact they had on your team or project.
✨Communicate Like a Pro
Strong communication skills are key, especially when working with non-technical users. Practice explaining complex AI concepts in simple terms. Think about how you can demonstrate your ability to guide and coach others on AI tools and processes during the interview.
✨Be Ready for Real-World Scenarios
Expect questions that put you in real-world situations where you need to apply your skills. Prepare to discuss how you would approach building internal AI-powered tools or integrating datasets. Show them you can think on your feet and deliver solutions that meet business needs.