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- 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 environment.
- Other info: High ownership role with excellent visibility and growth opportunities.
- Why this job: Shape the future of AI with live systems that make a difference.
- Qualifications: Strong Python skills and experience deploying LLM systems in production.
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) in London employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote) in London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the game. Building relationships can open doors that job applications alone can't.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects, especially those involving LLMs or AI systems. Share your GitHub or any live demos to give potential employers a taste of your work.
✨Ace the Interview
Prepare for technical interviews by brushing up on your Python skills and understanding LLM trade-offs. Practice common interview questions and be ready to discuss your past projects in detail. Confidence is key!
✨Apply Through Our Website
We’ve got some fantastic opportunities waiting for you! Make sure to apply through our website to get the best chance at landing that Machine Learning Engineer role. We’re excited to see what you bring to the table!
We think you need these skills to ace Machine Learning Engineer (hybrid or remote) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Machine Learning Engineer. Highlight your experience with LLMs, Python, and any relevant projects that showcase your ability to deliver real-world AI solutions.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background aligns with our mission at StudySmarter. Share specific examples of your work that demonstrate your hands-on experience in deploying AI systems.
Showcase Your Projects: Include links or descriptions of projects where you've built and deployed AI agents or workflows. We want to see evidence of your impact and how you've contributed to scalable systems in production environments.
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.
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 LLM systems. Brush up on your experience with LangChain or similar frameworks, as you’ll want to demonstrate your hands-on skills and how they apply to real-world scenarios.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built and deployed AI systems. Highlight the measurable impact these projects had, focusing on how you owned them from concept to production. This will show your potential employer that you can deliver value, not just theory.
✨Understand the Business Impact
Be ready to talk about how your work in AI has driven business outcomes. Think about examples where your systems improved efficiency or decision-making processes. This will help you align your technical skills with the company’s goal of delivering measurable business impact.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s AI initiatives and their approach to deploying systems at scale. This shows your genuine interest in the role and helps you gauge if the company’s environment aligns with your career goals.