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- Tasks: Build production-grade AI systems and integrate LLMs into large-scale platforms.
- Company: Global investment bank with a focus on innovative technology.
- Benefits: Competitive daily rate, flexible onsite work, and potential for contract to permanent.
- Other info: Dynamic environment with opportunities for career advancement.
- Why this job: Join a high-impact team and create real AI solutions that matter.
- Qualifications: Strong backend or data engineering experience with LLMs in production.
We are building a production-grade AI platform inside a global investment bank. This is not a research team or a chatbot project. This is engineering-heavy, focused on integrating LLM systems into large-scale, distributed environments used by trading and quant teams.
We are hiring across multiple levels:
- Lead Engineer (Agentic / RAG focus)
- Architect (end-to-end platform design - Agentic / RAG focus)
- Senior Engineers (x3) Agentic / RAG focus
- Potential Director-level hire to oversee the function
What This Role Requires (Read Before Applying)
- Strong background in backend or data engineering
- Proven experience working on production systems with real users
- Hands-on experience with LLMs in production (RAG, embeddings, etc.)
If your experience is limited to courses, side projects, or chatbot builds, this role is not a fit.
What You’ll Be Working On
- LLM-powered systems integrated into enterprise-grade platforms
- RAG pipelines over large internal datasets (structured + unstructured)
- High-throughput, low-latency services
- Multi-step workflows combining LLMs, data pipelines, and external tools
- Systems requiring reliability, observability, and auditability
Core Technical Requirements
- Strong Python (Java/Scala also relevant)
- Experience with distributed systems, APIs, microservices
- Familiarity with event-driven systems (Kafka or similar)
- LLM experience: RAG embeddings vector search / semantic retrieval
Strong Preference
- Experience working in high-scale environments (tech, fintech, data platforms)
- Exposure to regulated or high-control systems
- Experience designing or owning end-to-end systems
What This Role Is NOT
- Not a prompt engineering role
- Not a research position
- Not suitable for candidates without large-scale system experience
Why This Role
- Build real AI systems in production, not demos
- Join early in a high-impact, well-funded build
- Work alongside senior engineers and platform teams
Apply if you’ve built systems that operate at scale and have deployed LLM solutions into production, we want to hear from you.
Artificial Intelligence Engineer employer: CipherTek Recruitment
Contact Detail:
CipherTek Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and finance sectors. Attend meetups or webinars where you can chat with industry folks. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your LLM projects and any production systems you've worked on. This is your chance to demonstrate your hands-on experience and make a lasting impression.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and distributed systems knowledge. Practice coding challenges and system design questions that are relevant to the role. We want you to feel confident when it’s time to shine!
✨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, we love hearing from candidates who are genuinely excited about building real AI systems in production.
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with LLMs and production systems. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about building AI systems in a global investment bank. Let us know how your background makes you a perfect fit for our team.
Showcase Your Technical Skills: Be specific about your technical expertise, especially in Python, distributed systems, and APIs. We’re looking for hands-on experience, so mention any relevant projects that demonstrate your capabilities.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at CipherTek Recruitment
✨Know Your Tech Inside Out
Make sure you’re well-versed in the core technical requirements listed in the job description. Brush up on your Python skills, and be ready to discuss your experience with distributed systems, APIs, and LLMs. Prepare specific examples of how you've implemented these technologies in production environments.
✨Showcase Real-World Experience
This role is all about practical application, so come prepared with concrete examples of your work on production systems. Highlight any projects where you’ve integrated LLMs or built high-throughput services. Be ready to explain the challenges you faced and how you overcame them.
✨Understand the Business Context
Since this position is within a global investment bank, it’s crucial to understand the financial landscape and how AI can impact capital markets. Research the company’s operations and think about how your skills can contribute to their goals. This will show your genuine interest and help you stand out.
✨Prepare for Technical Questions
Expect in-depth technical questions that assess your problem-solving abilities and understanding of complex systems. Practice coding challenges related to backend engineering and distributed systems. You might also face scenario-based questions, so think through how you would approach real-world problems in a high-scale environment.