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- Tasks: Design and deploy intelligent AI systems that automate workflows and enhance decision-making.
- Company: Join a global leader in AI innovation with a production-first engineering culture.
- Benefits: Competitive contract rate, hybrid work model, and the chance to shape future AI technologies.
- Other info: Collaborate with top engineers and data teams to drive enterprise AI transformation.
- Why this job: Make a real impact by building scalable AI systems used worldwide.
- Qualifications: Experience with LLMs, AI systems, and strong programming skills in Python.
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.
Marketing Data Scientist employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Marketing Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI systems. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common data science scenarios and be ready to discuss how you've tackled real-world challenges in AI deployment.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who can help us build the future of AI. Your next big opportunity could be just a click away!
We think you need these skills to ace Marketing Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with LLMs, AI systems, and any relevant frameworks like LangChain or TensorFlow. 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 explain why you're passionate about AI and how your background makes you a perfect fit for this role. Don’t forget to mention specific projects where you've deployed real-world AI solutions.
Showcase Your Projects: If you've worked on any AI projects, especially those involving production-grade systems, make sure to include them in your application. We love seeing tangible examples of your work and how you've tackled complex problems in the past.
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 this exciting opportunity. Plus, it shows you’re keen to join 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 specific projects where you've deployed these technologies, as well as the frameworks like LangChain or TensorFlow that you've used. This will show them you're not just a theorist but someone who can get things done.
✨Showcase Your Problem-Solving Skills
Prepare to talk about complex problems you've solved in previous roles, especially those involving AI systems. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help you clearly demonstrate your impact and how you approach challenges.
✨Get Hands-On with the Tech
If you can, try to build a small project or prototype using the tools mentioned in the job description, like RAG pipelines or multi-agent orchestration. Being able to discuss a real-world application of your skills will set you apart from other candidates.
✨Understand Their Business Impact
Research the company’s current AI initiatives and think about how your work could enhance their operations. Be prepared to discuss how you can contribute to automating workflows and improving decision-making processes. Showing that you understand the business side of AI will impress them.