At a Glance
- Tasks: Design and build innovative AI systems that learn and adapt over time.
- Company: Cutting-edge tech firm focused on AI solutions for pharma, finance, and defence.
- Benefits: Flexible hybrid/remote work, competitive salary, and opportunities for professional growth.
- Other info: Join a dynamic team where your contributions directly impact real-world applications.
- Why this job: Tackle exciting engineering challenges and shape the future of AI technology.
- 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:
- As Engineer #2 on the AI side, you'll have 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. This is genuinely open territory, here and at most companies building in this space.
- 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:
- You've shipped multi-agent systems in production (router patterns, real failure modes, instrumentation, not demos).
- You've thought hard about how knowledge gets captured, verified, and promoted, and what goes wrong.
- You've built MCP servers with auth scoping and dynamic tool discovery.
- You know when to use multi-hop graph traversal versus vector search because you've built both.
- 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 Slough employer: ENAIBLE TALENT
As an Artificial Intelligence Engineer at our innovative company, you'll be part of a dynamic team dedicated to solving complex engineering challenges in the AI space. We offer a hybrid work environment that promotes flexibility and collaboration, alongside opportunities for professional growth and development in cutting-edge technologies. Our culture fosters creativity and encourages you to take ownership of your projects, making it an exciting place for those looking to make a meaningful impact in industries like pharma, finance, and defence.
StudySmarter Expert Advice🤫
We think this is how you could land Artificial Intelligence Engineer in Slough
✨Tip Number 1
Network like a pro! Reach out to folks in the AI 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 past projects, especially those involving memory write-back or multi-agent orchestration. Having tangible examples of your work can really set you apart when chatting with potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and understanding production-level challenges. Practice explaining complex concepts like graph traversal and conflict resolution clearly and confidently. We want you 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 seeing candidates who are proactive about their job search!
We think you need these skills to ace Artificial Intelligence Engineer in Slough
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 that you’re genuinely excited about tackling the challenges in agent orchestration and memory systems. Share any personal projects or experiences that highlight your passion!
Tailor Your Application:Make sure to customise your application to reflect the specific skills and experiences mentioned in the job description. Highlight your experience with multi-agent systems and production Python, as these are key areas for us. A tailored application shows us you’ve done your homework and are serious about joining our team.
Be Clear and Concise:While we love a good story, keep your application clear and to the point. Use bullet points where appropriate to make it easy for us to see your qualifications at a glance. Remember, we’re looking for specific experiences, so don’t be shy about showcasing them!
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 gives you a chance to explore more about what we do and how you can fit into our team!
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 key for this role, make sure you're comfortable with type hints, structured logging, and async FASTAPI. If you have experience with LLMs or MCP servers, be prepared to discuss how you've implemented these technologies in your work.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare some thoughtful questions about the company's approach to agent orchestration and how they handle data compliance. This not only shows your interest but also helps you gauge if the company aligns with your career goals.
