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- Tasks: Design and deploy intelligent AI systems that automate workflows and enhance decision-making.
- Company: Join a leading global organisation shaping the future of AI.
- Benefits: Competitive rate, hybrid work model, and opportunity to influence enterprise AI.
- Other info: Collaborative culture with excellent career growth opportunities in AI engineering.
- Why this job: Make a real impact by building production-grade AI systems at scale.
- Qualifications: Experience with LLMs, AI systems, and strong programming skills required.
Hybrid – London (3 days office - 2 days remote)
Rate: 500-550 Outside IR 35
Contract: 6 months initially
Build Production-Grade AI Systems at Scale
We’re Looking for AI Builders — Not Just Experimenters. If you’ve deployed real-world LLMs, built autonomous AI agents, and engineered scalable AI systems that people actually use, this is the opportunity to shape the future of AI across a global organisation.
We’re building next-generation AI capabilities across both:
- AI-powered SaaS / B2B products
- Enterprise-wide AI transformation initiatives
You’ll work on high-impact systems that automate workflows, enhance decision-making, and deliver measurable business value at scale.
What You’ll Build
You’ll design and deploy intelligent AI systems powered by:
- Large Language Models (LLMs)
- Agentic AI frameworks
- Retrieval-Augmented Generation (RAG)
- Multi-agent orchestration
- Tool-using autonomous workflows
This is a hands-on engineering role focused on production delivery, scalability, reliability, and business impact.
Your Work Will Include
- Building AI agents with reasoning, planning, memory, and tool orchestration
- Developing advanced RAG pipelines and context-aware AI systems
- Designing MCP-style architectures and interoperable AI workflows
- Creating recommendation, forecasting, and classification models on large-scale datasets
- Automating complex business operations using AI-driven decision systems
- Integrating AI into APIs, enterprise platforms, and customer-facing products
- Optimising latency, inference performance, observability, and cost efficiency
What We’re Looking For
We want engineers and scientists who can take AI from concept to production.
Strong Experience In
- LLMs, GenAI, and Agentic AI systems
- LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar frameworks
- RAG pipelines and vector databases
- AI agents and multi-agent orchestration
- Python, PyTorch, TensorFlow, Scikit-learn
- Cloud AI platforms such as AWS, Azure, or GCP
- Production deployment, MLOps, and scalable AI infrastructure
- API integration and workflow automation
Bonus Points For
- MCP / Model Context Protocol experience
- Fine-tuning and evaluation frameworks
- Recommendation systems and forecasting models
- Real-world enterprise AI transformation experience
- Experience balancing model quality, latency, and operational cost
Why Join Us?
- Work on AI systems used at global scale
- Join a production-first AI engineering culture
- Build technology that directly impacts products, operations, and business strategy
- Collaborate with strong engineering, product, and data teams
- Influence how enterprise AI is designed and deployed across a global organisation
If you enjoy solving complex problems, deploying real AI systems, and building beyond prototypes, this role offers the opportunity to make a genuine impact.
Data scientist (marketing) employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data scientist (marketing)
✨Tip Number 1
Network like a pro! Connect with people in the industry on LinkedIn, attend meetups, and join relevant online communities. The more you engage, the better your chances of hearing about opportunities before they even hit the job boards.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and approach real-world problems!
✨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, it shows you’re genuinely interested in joining our team and contributing to our exciting AI initiatives.
We think you need these skills to ace Data scientist (marketing)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match our job description. Highlight your work with LLMs, AI systems, and any relevant projects you've completed. We want to see how you can bring your unique expertise to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you're passionate about AI and how your background aligns with our mission. Be specific about your experience with production-grade AI systems and how you’ve made an impact in previous roles.
Showcase Your Projects: If you've built or deployed any AI systems, make sure to include them in your application. We love seeing real-world examples of your work, especially if they involve LLMs or multi-agent orchestration. This helps us understand your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Russell Tobin
✨Know Your AI Stuff
Make sure you brush up on your knowledge of LLMs, GenAI, and Agentic AI systems. Be ready to discuss your hands-on experience with frameworks like LangChain or PyTorch, as well as any real-world projects you've worked on that showcase your ability to take AI from concept to production.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex problems in previous roles. Think about times when you designed and deployed intelligent AI systems or automated workflows, and be ready to explain your thought process and the impact of your solutions.
✨Get Familiar with Their Tech Stack
Research the tools and technologies mentioned in the job description, such as RAG pipelines and cloud platforms like AWS or Azure. Being able to speak confidently about these technologies will show that you're not just a fit for the role but also genuinely interested in their work.
✨Ask Insightful Questions
Prepare some thoughtful questions to ask during the interview. This could be about their current AI projects, team dynamics, or how they measure success in their AI initiatives. Asking the right questions shows your enthusiasm and helps you gauge if the company is the right fit for you.