At a Glance
- Tasks: Design and build AI agents and workflows using cutting-edge LLM technology.
- Company: Join a leading AI company focused on real-world impact.
- Benefits: Competitive daily rate, hybrid work options, and a supportive team environment.
- Other info: Fast-paced environment with high ownership and visibility in projects.
- Why this job: Make a difference by deploying live AI systems that drive business transformation.
- Qualifications: Strong Python skills and experience with LLM-powered systems required.
Location: London (Hybrid)
Contract: Outside IR35
Rate: £500–£550 per day (depending on interview outcome)
We’re looking for AI operators who ship — not experiment. This is an opportunity to join a major AI build focused on deploying real-world LLM and agentic systems at scale across both AI products and enterprise transformation initiatives. You’ll be working in a production-first environment where the emphasis is on building reliable, scalable AI systems that deliver measurable business impact.
What You’ll Be Working On:
- Designing and building AI agents and agentic workflows powered by LLMs
- Developing systems using RAG, reasoning, planning, memory, and tool orchestration
- Building multi-step intelligent systems capable of real-world tool usage
- Working with MCP-style architectures (or equivalent) to structure context and improve interoperability
- Contributing to recommendation, classification, and forecasting systems using large-scale datasets
- Automating business workflows and decision-making processes through AI-driven solutions
What You’ll Be Doing:
- Owning projects end-to-end from concept through to production deployment and iteration
- Building and deploying AI agents that operate reliably in production environments
- Integrating AI systems into APIs, products, and operational workflows
- Collaborating closely with engineering teams to ensure scalability, observability, and maintainability
- Making pragmatic decisions balancing model performance, latency, and cost efficiency
Core Requirements:
- Strong Python skills with experience writing production-grade code
- Proven experience deploying LLM-powered systems into production environments
- Hands-on experience with LangChain, LangGraph, or equivalent orchestration frameworks
- Experience building AI agents and agentic workflows with tool usage and multi-step reasoning
- Strong understanding and implementation experience of RAG systems
- Familiarity with MCP/FastMCP/FastAPI or similar orchestration patterns
- Strong understanding of LLM trade-offs including hallucination mitigation, latency, and cost optimisation
- Experience deploying AI systems in cloud environments such as AWS, GCP, or Azure
- Working knowledge of SQL/data manipulation (Working knowledge of SQL or data manipulation is expected, but it is not a primary focus for this role.)
Strong signals include:
- Experience working on SaaS or B2B AI products or delivering AI-driven transformation within an organisation.
- A background in high-growth or scaling environments, where speed and pragmatism are critical.
- Clear evidence of systems that are actively used and delivering value, rather than experimental work.
Ideal Background:
- Masters degree or higher in Computer Science, Mathematics, Engineering, or a related technical field
- Experience building and iterating on AI systems delivering measurable value
- Strong ownership mindset and ability to operate in fast-moving environments
- Product-focused approach with a bias toward delivering impact
Why This Role:
- Work on live AI systems used at scale
- Join a well-supported AI engineering environment
- High ownership and visibility across products and operations
- Opportunity to shape enterprise AI adoption in a meaningful way
Machine Learning Engineer (hybrid or remote) employer: Russell Tobin
Join a forward-thinking company that prioritises innovation and real-world impact in the AI sector. With a hybrid work model based in London, you'll benefit from a collaborative culture that encourages ownership and visibility in your projects, alongside competitive rates and opportunities for professional growth. This role offers the chance to work on live AI systems that are actively transforming businesses, making it an ideal environment for those looking to make a meaningful contribution in a fast-paced industry.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote)
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow AI enthusiasts. 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 agents. This will give potential employers a taste of what you can do and how you can contribute to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with deploying AI systems and how you've tackled challenges in production environments.
✨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 reliable AI systems. Your next big opportunity could be just a click away!
We think you need these skills to ace Machine Learning Engineer (hybrid or remote)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your Python prowess, experience with LLMs, and any relevant projects you've worked on. We want to see how you can contribute to our AI build!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for AI and how your background aligns with our mission at StudySmarter. Be specific about your achievements and how they relate to the role of Machine Learning Engineer.
Showcase Real-World Impact:We’re all about shipping real solutions, not just experiments. In your application, include examples of AI systems you've built that have delivered measurable business impact. This will help us see your practical experience in action!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen to join our team at StudySmarter!
How to prepare for a job interview at Russell Tobin
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and any orchestration frameworks like LangChain or LangGraph. Brush up on your experience with deploying LLM-powered systems and be ready to discuss specific projects where you’ve made a measurable impact.
✨Showcase Real-World Applications
Prepare to talk about how you've built and deployed AI systems that are actively used, not just theoretical projects. Highlight your experience in production environments and how your work has contributed to business outcomes. This will demonstrate your ability to ship rather than just experiment.
✨Be Ready for Technical Challenges
Expect technical questions that assess your problem-solving skills and understanding of AI systems. Practice explaining complex concepts like RAG systems and multi-step reasoning in simple terms. This will show your depth of knowledge and your ability to communicate effectively with non-technical stakeholders.
✨Demonstrate Ownership and Impact
Emphasise your ownership mindset by discussing projects where you took the lead from concept to deployment. Share examples of how you balanced model performance, latency, and cost efficiency, and be prepared to discuss the tangible value your work delivered. This aligns perfectly with the role's focus on delivering measurable business impact.
