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- Tasks: Join us to create innovative AI demos and optimise prompt engineering for cutting-edge trading technology.
- Company: We're a top-rated trading platform expanding globally with award-winning products and a talented team.
- Benefits: Enjoy flexible work options, a collaborative culture, and the chance to work with the latest tech.
- Why this job: Be at the forefront of AI innovation while having fun and making a real impact in the industry.
- Qualifications: Master’s in AI/Computer Science, strong Python skills, and hands-on experience with LLM APIs required.
- Other info: Willingness to travel for team events is a plus; open-source contributions are highly valued.
The predicted salary is between 36000 - 60000 ÂŁ per year.
We are a leading trading platform that is ambitiously expanding to the four corners of the globe. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We deliver only the best, so we are always in search of the best people to join our ever-growing talented team. We’re spinning up a brand-new AI department and need a first-on-the-ground engineer who can turn wild LLM ideas into working demos overnight, teach everyone around you why automation matters, and have fun doing it. If you can ship Python or TypeScript prototypes before the pizza gets cold, keep reading.
Responsibilities:
- Prompt Engineering & Orchestration: Craft, iterate, and optimize prompts for APIs (OpenAI, Cohere, Anthropic). Build multi-step “chains” using LangChain, LlamaIndex, or custom controllers. Develop and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling.
- Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features.
- Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback.
- MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows.
- Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end.
Requirements:
- Master’s degree in AI and/or Computer Science.
- Hands-on experience integrating LLM APIs (e.g. OpenAI, Hugging Face Inference).
- Practical experience fine-tuning LLMs via OpenAI, HuggingFace or similar APIs.
- Strong proficiency in Python.
- Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex.
- Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines.
- Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos.
- Familiarity with containerization (Docker) and at least one orchestration/deployment platform.
- Excellent communication and ability to frame AI solutions in business terms.
Nice-to-have:
- Familiarity with database systems (PostgreSQL, MongoDB) and caching layers (Redis).
- Open-source contributions or published AI demos.
- Understanding of cost-optimization, monitoring for LLM usage and model-API trade-offs.
- Familiarity with prompt engineering best practices and “vibe-coding” tools (e.g., GitHub Copilot, Cursor IDE).
- Willingness to travel occasionally for team offsites or workshops.
AI Product and Research Engineer employer: capital.com
Contact Detail:
capital.com Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Product and Research Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in AI and LLM technologies. Being well-versed in current trends will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Engage with the AI community through forums, webinars, or local meetups. Networking can lead to valuable insights and connections that may give you an edge when applying for this role.
✨Tip Number 3
Showcase your rapid prototyping skills by creating a portfolio of interactive demos using tools like Streamlit or Gradio. This hands-on evidence of your capabilities can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss how you've framed AI solutions in business terms in past projects. Being able to communicate the value of your work effectively is crucial for this role.
We think you need these skills to ace AI Product and Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI, Python, and prompt engineering. Use keywords from the job description to demonstrate that you meet the specific requirements.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your ability to turn ideas into prototypes quickly. Mention any relevant projects or experiences that align with the responsibilities outlined in the job description.
Showcase Your Projects: Include links to any AI demos or projects you've worked on, especially those involving rapid prototyping or LLM APIs. This will give the hiring team a clear view of your skills and creativity.
Highlight Collaboration Skills: Emphasise your experience in cross-functional collaboration and communication. Provide examples of how you've worked with teams to deliver features or solve problems, as this is crucial for the role.
How to prepare for a job interview at capital.com
✨Showcase Your Prototyping Skills
Be prepared to discuss your experience with rapid prototyping, especially using tools like Streamlit or Gradio. Bring examples of past projects where you turned ideas into working demos quickly, as this aligns perfectly with the role's requirements.
✨Demonstrate Your Prompt Engineering Expertise
Since prompt engineering is a key responsibility, be ready to explain your approach to crafting and optimising prompts for APIs. Discuss any specific challenges you've faced and how you overcame them, showcasing your deep understanding of the subject.
✨Highlight Your Collaboration Experience
This role involves cross-functional collaboration, so share examples of how you've worked with different teams in the past. Emphasise your communication skills and ability to frame AI solutions in business terms, as this will resonate well with the interviewers.
✨Familiarity with Tools and Technologies
Make sure to brush up on the technologies mentioned in the job description, such as Docker, Kubernetes, and vector databases like Pinecone or FAISS. Being able to discuss your hands-on experience with these tools will demonstrate your readiness for the role.
