At a Glance
- Tasks: Design and implement AI systems using cutting-edge technologies and collaborate across teams.
- Company: Join SLR, a forward-thinking company focused on real-world AI solutions.
- Benefits: Enjoy meaningful ownership, competitive salary, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on practical building and continuous learning.
- Why this job: Shape the future of intelligent software and tackle real engineering challenges.
- Qualifications: 2-5 years in software or AI engineering with strong backend skills.
The predicted salary is between 60000 - 80000 € 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
- Implement AI Infrastructure
- 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
- Develop MCP and Tool Integration Layers
- 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
- Ship Production Code
- 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
- Collaborate Across Teams
- 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
- LLM Application Development
- 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
- Infrastructure and Deployment
- Hands‑on experience deploying production systems
- Docker and containerization
- Cloud platforms (AWS, GCP, or Azure)
- CI/CD pipelines
- Scalable service architecture
- Data and Retrieval Systems
- 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 Manchester employer: SLR Consulting
SLR is an exceptional employer for AI Engineers, offering a dynamic work environment where innovation thrives. With a strong focus on practical application and real-world problem-solving, employees enjoy meaningful ownership and autonomy in their projects. The collaborative culture fosters growth and learning, while the opportunity to work with cutting-edge AI technologies ensures that your contributions have a significant impact on the company's success.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer in Manchester
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI space. Attend meetups, webinars, or even online forums. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs or AI systems. Having tangible evidence of your work can really set you apart when chatting with potential employers.
✨Tip Number 3
Don’t just apply—engage! When you find a role that excites you, reach out to current employees on LinkedIn. Ask them about their experiences and what they love about working there. This can give you valuable insights and make your application stand out.
✨Tip Number 4
Keep it real! During interviews, be ready to discuss how you've tackled real-world problems with your AI skills. Share specific examples of your work, focusing on how you moved from concept to production. This shows you're not just theory-based but a practical builder!
We think you need these skills to ace AI Engineer in Manchester
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 along 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. This will show you’re a team player who values input from others.
✨Problem-Solving Mindset
Be prepared to tackle some real-world problems during the interview. They might ask you to think through a scenario involving AI system design or troubleshooting. Approach these questions with a clear, logical thought process, and don’t hesitate to share how you would iterate based on user feedback.
