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For Companies At a Glance
- Tasks: Design and build AI agents and workflows that make a real-world impact.
- Company: Join a leading AI company focused on deploying scalable systems.
- Benefits: Competitive daily rate, hybrid work model, and opportunities for growth.
- Other info: Fast-paced environment with high ownership and visibility in projects.
- Why this job: Work on live AI systems and shape the future of enterprise AI.
- 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 Remote in City of 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 Remote in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space on LinkedIn or at meetups. 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 think.
✨Tip Number 3
Prepare for interviews by brushing up on real-world applications of your skills. Be ready to discuss how you've deployed AI systems in production and the impact they had. We want to see that you can ship, not just experiment!
✨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, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Machine Learning Engineer - Hybrid Remote in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with LLMs, Python, and any relevant projects that showcase your ability to build scalable AI systems. 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 explain why you’re passionate about AI and how your background makes you a perfect fit for our team. Don’t forget to mention specific projects or experiences that demonstrate your hands-on skills.
Showcase Real-World Impact: When detailing your past work, focus on projects that had measurable outcomes. We love to see evidence of systems that are actively used and delivering value, rather than just experimental work. This will help us understand your impact in previous roles.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining 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 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 Real-World Impact
Prepare to discuss specific projects where you've deployed AI systems that delivered measurable value. Highlight your role in these projects and be ready to explain how your contributions made a difference, especially in production environments.
✨Be Ready for Technical Questions
Expect technical questions that dive deep into your understanding of RAG systems, multi-step reasoning, and cloud deployments. Practise explaining complex concepts in simple terms, as this will show your ability to communicate effectively with both technical and non-technical stakeholders.
✨Demonstrate Your Ownership Mindset
During the interview, convey your ownership mindset by discussing how you’ve taken projects from concept to deployment. Share examples of how you’ve navigated challenges and made pragmatic decisions to ensure success in fast-paced environments.