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For Companies At a Glance
- Tasks: Design and implement AI systems using cutting-edge technologies and collaborate with diverse teams.
- Company: SLR, a forward-thinking company focused on real-world AI solutions.
- Benefits: Competitive salary, autonomy in projects, and opportunities for professional growth.
- Other info: Dynamic environment with meaningful ownership and rapid project progression.
- Why this job: Shape the future of intelligent software and tackle real engineering challenges.
- Qualifications: 2-5 years in software or AI engineering, strong Python skills, and a passion for building.
The predicted salary is between 60000 - 80000 ÂŁ per year.
SLR is seeking an AI Development Engineer who enjoys building AI systems that operate reliably in the real world. This role sits at the intersection of AI engineering, software development, and infrastructure, focusing on designing and implementing production‑grade systems powered by large language models (LLMs). You will work hands‑on across the full delivery lifecycle—moving quickly from concept to prototype to production. Working closely with product, engineering, and data teams, you will help deliver intelligent applications built on modern AI infrastructure. We value practical builders over academic theory. Success in this role is defined by your ability to design, implement, deploy, and operate real systems that deliver business value.
What You Will Build
- Design and implement systems across the AI stack, including:
- LLM-powered applications and intelligent agents
- Model orchestration and tool‑use frameworks
- Retrieval systems and knowledge layers (RAG)
- MCP‑style integration layers connecting models to tools, APIs, and data sources
- Scalable infrastructure supporting AI workloads
Your work will progress rapidly from prototype to production, with real users and real constraints.
Key Responsibilities
- Build AI Systems
- Design and implement production‑grade systems powered by LLMs and modern AI frameworks
- Develop applications using technologies such as OpenAI, Anthropic and other LLM APIs, LLM gateway, Vector databases, Agent orchestration frameworks
- Build and operate the infrastructure required to run reliable AI services, including API services supporting AI applications, orchestration layers between models and tools, retrieval pipelines and knowledge indexing, observability and monitoring for AI systems, scalable backend services
- Design integration layers that enable models to interact with external systems, including API integrations, tool‑use systems for agents, connectors to databases, SaaS tools, or internal platforms, structured prompting and function‑calling architectures
- Move quickly from concept to working product
- Write clean, maintainable backend code
- Build testable services
- Deploy systems in production environments
- Iterate based on real user feedback
- Work closely with product managers, engineers, and designers to turn ideas into working solutions
Required Skills
- Software Engineering Foundations
- Strong backend engineering experience
- Proficiency in Python (preferred) or TypeScript
- Experience building REST APIs and backend services
- Solid system design fundamentals
- Debugging and production troubleshooting skills
- Understand software development lifecycle
- Experience building applications using large language models
- Prompt engineering and structured prompting
- Tool use and function calling
- Retrieval‑Augmented Generation (RAG) architectures
- LLM evaluation and iterative improvement
- Hands‑on experience deploying production systems
- Docker and containerization
- Cloud platforms (AWS, GCP, or Azure)
- CI/CD pipelines
- Scalable service architecture
- Experience building and operating knowledge layers
- Vector databases (e.g. Pinecone, Weaviate, pgvector)
- Document ingestion pipelines
- Embedding workflows
- Search and retrieval optimization
Nice To Have Experience With
- MCP architectures or tool‑connected AI systems
- Agent frameworks
- Knowledge graph systems
- Streaming or event‑driven systems
- Distributed systems design
- Evaluation frameworks for AI systems
What We Look For
- Prefer building working systems over discussing them
- Move quickly while maintaining quality
- Enjoy solving messy, real‑world problems
- Take ownership from prototype through to production
- Stay curious about emerging AI capabilities
You do not need to know everything—but you should be comfortable learning quickly and shipping continuously.
Experience
2–5 years of experience in software engineering, AI engineering, or ML systems.
We Value Evidence Of Building, Including
- Shipped products
- Real systems running in production
- Open‑source contributions
- Side projects and experimentation
Demonstrated delivery matters more than credentials.
Why Join SLR
- Meaningful ownership and autonomy
- Real engineering challenges
- The opportunity to shape how intelligent software is designed, built, and deployed across SLR
AI Engineer in Bristol employer: SLR Consulting
Contact Detail:
SLR Consulting Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer in Bristol
✨Tip Number 1
Network like a pro! Reach out to folks in the AI engineering space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI systems. We love seeing real-world applications, so make sure to highlight any production code you've shipped.
✨Tip Number 3
Prepare for technical interviews by brushing up on your backend engineering skills. We recommend practicing coding challenges and system design questions, as they’re often key in landing roles like AI Engineer.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always on the lookout for passionate builders who want to tackle real-world problems with us.
We think you need these skills to ace AI Engineer in Bristol
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI shine through! We want to see that you’re not just ticking boxes but genuinely excited about building real-world AI systems. Share any personal projects or experiences that highlight your passion.
Be Specific About Your Skills: Make sure to detail your experience with the technologies mentioned in the job description. If you've worked with LLMs, Python, or cloud platforms, give us the juicy details! We love seeing how your skills align with what we need.
Keep It Clear and Concise: We appreciate a well-structured application that gets straight to the point. Avoid fluff and focus on what makes you a great fit for the role. Use bullet points if it helps to make your achievements stand out!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it’s super easy to do!
How to prepare for a job interview at SLR Consulting
✨Know Your AI Stuff
Make sure you brush up on your knowledge of large language models and the technologies mentioned in the job description. Be ready to discuss your experience with LLMs, API integrations, and any relevant projects you've worked on. This shows you're not just familiar with the theory but have practical experience too.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built or deployed AI systems. Highlight any real-world applications you've developed, especially those that demonstrate your ability to move from concept to production. Bring examples of your code or even a demo if possible!
✨Collaboration is Key
Since this role involves working closely with product managers and engineers, be ready to discuss how you've collaborated in past projects. Share examples of how you’ve turned ideas into working solutions and how you handle feedback from team members.
✨Stay Curious and Adaptable
SLR values candidates who are eager to learn and adapt. Be prepared to discuss how you keep up with emerging AI technologies and how you've applied new knowledge in your work. Show them that you're not just a builder but also a continuous learner!