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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 transformation 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 in a dynamic, innovative environment.
- Why this job: Make a real impact by building scalable AI systems used worldwide.
- Qualifications: Experience with LLMs, AI systems, and cloud platforms; strong coding 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 Marketing Scientist employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Marketing 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 coding challenges and be ready to discuss your past projects in detail, focusing on how they relate to real-world applications.
✨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 next-generation AI capabilities. Your dream job could be just a click away!
We think you need these skills to ace Data Marketing Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Marketing Scientist. Highlight your experience with LLMs, AI systems, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
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 makes you a perfect fit for our team. Be sure to mention specific technologies or frameworks you've worked with that relate to the job.
Showcase Your Projects: If you've built or deployed AI systems, don't hold back! Include links to your projects or GitHub repositories. We love seeing real-world applications of your work, especially if they demonstrate your ability to take AI from concept to production.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do!
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 PyTorch 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 Familiar with Their Tech Stack
Research the tools and platforms mentioned in the job description, like AWS, Azure, or GCP. If you have experience with MLOps or API integration, be ready to share examples of how you've used these in real-world applications. This shows you're aligned with their needs and can hit the ground running.
✨Ask Insightful Questions
Prepare thoughtful questions about their AI initiatives and how they measure success. This not only shows your interest in the role but also gives you a chance to assess if the company culture and projects align with your career goals. Plus, it makes for a more engaging conversation!