At a Glance
- Tasks: Design and build innovative AI systems that learn and adapt over time.
- Company: Join a cutting-edge tech company focused on AI for pharma, finance, and defence.
- Benefits: Enjoy a competitive salary, flexible remote work, and opportunities for professional growth.
- Other info: Be part of a dynamic team with a focus on solving complex problems.
- Why this job: Tackle exciting engineering challenges and make a real impact in the AI space.
- Qualifications: Experience with multi-agent systems and production-level Python development.
The predicted salary is between 60000 - 80000 £ per year.
Most agent systems are stateless between sessions, or bolt on memory as an afterthought; memory is the product. We build the governed context layer for the agentic enterprise, turning complex, heterogeneous enterprise data into context graphs that power LLM reasoning pipelines for clients in pharma, finance, and defence. Our graph is designed to get smarter over time: every agent interaction, confirmed inference, and discovered rule feeds back as permanent knowledge. How that happens reliably and faithfully is an open design challenge, and the most interesting engineering problem on the team.
What you'll own:
- Architectural ownership of four areas from day one:
- Memory write-back: Design the mechanism that turns agent sessions into durable graph knowledge. Faithfulness, conflict safety, and scale all need solving.
- Multi-agent orchestration: Own the router, specialist sub-agents, streaming traces, and memory tier handoffs. Build for real production failure modes, not the happy path.
- MCP integrations: Each enterprise system gets its own MCP server. Extend the connector library and own the gateway to client LLM stacks.
- Agent guardrails: Access control enforced at the data layer, LTN formal compliance logic, and provable constraints — not prompt-level suggestions.
What we're looking for:
- Experience shipping multi-agent systems in production (router patterns, real failure modes, instrumentation, not demos).
- Thoughtful about how knowledge gets captured, verified, and promoted, and what goes wrong.
- Experience building MCP servers with auth scoping and dynamic tool discovery.
- Knowledge of when to use multi-hop graph traversal versus vector search.
- Production Python is second nature: type hints, structured logging, async FASTAPI.
Bonus:
- Experience with faithfulness eval / LLM-as-judge, entity resolution, bitemporal data modelling, write-back conflict resolution, multi-tenant namespace design, or formal/neuro-symbolic constraints (LTN).
- Regulated-industry data experience is a strong plus.
The write-back problem, making an agent system that learns trustworthily at scale, is unsolved here and almost everywhere else. You'll own the architecture across formal logic, bitemporal graphs, and production LLM pipelines, at a company where that's the core product, not a side project.
Artificial Intelligence Engineer in London employer: ENAIBLE TALENT
As an Artificial Intelligence Engineer at our innovative company, you'll be part of a dynamic team that thrives on solving complex engineering challenges in a hybrid work environment. We offer a collaborative culture that prioritises employee growth through continuous learning opportunities and hands-on experience with cutting-edge technologies in the AI space. Located in a vibrant tech hub, we provide unique advantages such as access to industry leaders and a network of like-minded professionals, making it an excellent place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Artificial Intelligence Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech space, especially those who work with multi-agent systems. Attend meetups or webinars, and don’t be shy about sliding into DMs on LinkedIn. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to memory write-back and multi-agent orchestration. Use GitHub to share your code and document your thought process. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by diving deep into the challenges mentioned in the job description. Brush up on topics like conflict safety and dynamic tool discovery. Practise explaining complex concepts in simple terms, as this will help you stand out during technical interviews.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your experience with production Python and regulated-industry data, and let us know how you can tackle the write-back problem!
We think you need these skills to ace Artificial Intelligence Engineer in London
Some tips for your application 🫡
Show Your Passion for AI:When you're writing your application, let your enthusiasm for artificial intelligence shine through! We want to see how excited you are about tackling the challenges of memory and agent orchestration. Share any personal projects or experiences that highlight your love for the field.
Be Specific About Your Experience:We’re looking for concrete examples of your work with multi-agent systems and production environments. Don’t just say you’ve done it; tell us how you approached problems like knowledge capture and conflict resolution. The more details, the better!
Tailor Your Application:Make sure your application speaks directly to the job description. Highlight your experience with Python, LLMs, and any relevant technologies. We want to see how your skills align with our needs, so don’t be shy about connecting the dots!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at ENAIBLE TALENT
✨Know Your Stuff
Make sure you brush up on your knowledge of multi-agent systems and memory write-back mechanisms. Be ready to discuss your past experiences with production systems, especially how you've tackled real failure modes. This will show that you understand the complexities of the role.
✨Showcase Your Problem-Solving Skills
Prepare to dive into specific challenges you've faced in previous projects, particularly around knowledge capture and verification. Think about how you approached these problems and be ready to share your thought process. This will demonstrate your ability to tackle the open design challenges mentioned in the job description.
✨Familiarise Yourself with the Tech Stack
Since production Python is a must-have, make sure you're comfortable discussing type hints, structured logging, and async FASTAPI. If you have experience with LLMs or MCP servers, be prepared to talk about those too. Showing familiarity with the tools they'll be using can set you apart from other candidates.
✨Ask Insightful Questions
Prepare some thoughtful questions about the company's approach to agent orchestration and memory management. This not only shows your interest in the role but also gives you a chance to gauge if the company aligns with your career goals. Plus, it opens up a dialogue that can showcase your expertise.
