At a Glance
- Tasks: Design and optimise AI platforms for innovative agent-based products.
- Company: Join Travtus, a mission-driven startup transforming the Housing Industry with AI.
- Benefits: Competitive salary, private healthcare, pension, and Deliveroo allowance.
- Other info: Collaborative team culture with opportunities for innovation and growth.
- Why this job: Work with cutting-edge AI tech and make a real impact in a fast-paced environment.
- Qualifications: Experience in LLM applications, prompt engineering, and strong collaboration skills.
Travtus is looking for an AI Engineer to help design, build, and optimise the core AI platform capabilities that power agent-based products and workflows. This role is focused on platform development and is responsible for shaping how LLM-powered agents are designed, orchestrated, tested, monitored, and improved in production. The role sits at the intersection of prompt engineering, agent architecture, solution design, and platform capability development. You will define how agents interact with tools, APIs, and downstream services, and help specify the requirements and contracts for the services they depend on. You will work closely with platform engineers, who build the APIs and services consumed by agents, and with product engineers, helping ensure agent capabilities support strong user experience and front-end design.
This role also includes working on patterns where prompt outputs are used as code to support interface generation, front-end behaviour, and rapid product iteration. You will also own the LLM and prompt tooling lifecycle, from prompt management through evaluation, testing, and continuous improvement. This is a hands-on senior role for someone who combines deep technical skill with strong systems thinking and a practical approach to building scalable, usable, and production-ready AI capabilities.
Key Responsibilities
- Design and develop multi-agent prompts and orchestration patterns
- Define how agents, tools, APIs, services, and platform components interact, including requirements, contracts, and integration patterns
- Work alongside platform engineers to define the APIs and services that agents consume and use
- Work closely with product engineers to support user experience and front-end design, including the use of prompt outputs as code
- Own LLM and prompt tooling across the full lifecycle, from prompt management and versioning through evaluation, testing, and optimisation
- Design requirements for downstream and supporting services that enable agent execution, workflow control, observability, and integration
- Lead the research, evaluation, and selection of frameworks for agent orchestration, tool use, evaluation, observability, and deployment
- Perform model selection based on use case fit, quality, latency, scalability, and cost
- Design and implement tools and function-calling patterns for agent-based systems
- Carry out prompt tuning and iterative optimisation of agent behaviour and output quality
- Monitor, debug, and continuously tune agents in development and production
- Design for performance, scalability, reliability, usability, maintainability, and cost efficiency
- Define and execute testing strategies for LLM agents and platform-level AI workflows
- Integration test agents end to end with other parts of the platform to ensure reliability across workflows, services, and user-facing experiences
- Build proofs of concept and prototypes to validate architectures, frameworks, and new platform capabilities
- Produce end-to-end solution designs for AI platform components and services
- Use AI to improve software development, including prototyping, implementation, testing, debugging, and engineering productivity
- Collaborate across engineering and product teams to translate business and technical goals into robust platform capabilities
Requirements
- The successful applicant will be a natural problem-solver with an affinity for automation, efficiency, and AI-driven solutions with excellent communication and collaboration skills across both technical and non-technical teams.
- Strong experience designing, building, and deploying LLM-based applications or agentic systems
- Demonstrated expertise in prompt engineering, prompt tuning, and structured output design
- Experience designing multi-agent workflows and orchestration logic
- Strong understanding of API design, service integration, and interface contracts
- Experience owning or working with prompt management, evaluation, and testing tooling
- Experience working with or defining requirements for platform services that support AI applications
- Experience evaluating and selecting models, frameworks, and AI tooling
- Practical experience with tool use, function calling, workflow control, and system integration
- Experience defining and implementing testing and evaluation strategies for LLM systems
- Experience with end-to-end and integration testing across complex platform workflows
- Strong understanding of performance optimisation, including latency, throughput, scalability, and cost trade-offs
- Experience monitoring and improving AI systems in production
- Strong software engineering skills and experience building production-grade systems
- Ability to work effectively with both product engineers and platform engineers
- Ability to produce clear solution designs and communicate technical decisions effectively
- Comfortable working across experimentation, architecture, and delivery
Nice to Have
- Experience with agent orchestration frameworks and evaluation platforms
- Familiarity with observability, tracing, and monitoring for AI systems
- Experience designing guardrails, controls, and failure handling for AI workflows
- Experience supporting front-end or UX-oriented engineering through AI-assisted design or generation patterns
- Experience with code generation or structured prompt outputs used in software delivery workflows
- Experience with cloud-native or distributed production environments
- Background in solution architecture, or applied AI systems
- Experience using AI to improve internal engineering productivity and delivery
What Success Looks Like
- The AI platform provides a strong, scalable foundation for agent-based products and workflows
- Agents, tools, APIs, and service integrations are reliable, testable, and production-ready
- LLM and prompt tooling supports disciplined prompt management, evaluation, testing, and continuous improvement
- Platform capabilities are designed with strong usability, maintainability, and operational robustness
- Agent outputs support high-quality product experiences, including front-end and UX use cases where appropriate
- Model, prompt, and framework choices are evidence-based and continuously optimised
- Agents are integration tested end to end with the wider platform and perform reliably across real workflows
- New ideas can be rapidly validated through POCs and evolved into durable platform capabilities
- Product engineers and platform engineers can work effectively around clear contracts, services, and orchestration patterns
- AI is used effectively both within the product and to improve software development practices
About the Team
We are a dedicated team, working in a truly collaborative style, where everyone is heard and brings something valuable to the conversation. Constantly pushing boundaries to create groundbreaking solutions for our customers. We are fundamentally challenging the way one of the largest industries in the world operates, and our commercial success pays testament to the skill, commitment and passion that our team displays every day. Working together in person fuels our best ideas and innovations, which is why all team members spend at least three days a week in our London office.
Benefits
- Be part of a growing, mission-driven startup with global ambitions
- Exposure to cutting-edge AI technology in a practical, high-demand sector
- Collaborative, fast-paced team culture with space to innovate and grow
- Full-time, salary: £60,000-80,000
- On-site working model with a modern London office (Farringdon)
- Private Healthcare
- Pension
- Deliveroo Allowance
AI Engineer employer: MetaProp
Travtus is an exceptional employer, offering a dynamic and collaborative work environment in the heart of London. As a mission-driven startup, we provide our AI Engineers with exposure to cutting-edge technology and ample opportunities for professional growth, all while fostering a culture that values innovation and teamwork. With competitive salaries, private healthcare, and a modern office space, we ensure our employees thrive both personally and professionally.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at MetaProp or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to MetaProp.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like MetaProp.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like MetaProp that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at MetaProp.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at MetaProp and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at MetaProp
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If MetaProp uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
