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- Tasks: Design and build AI agents and workflows that deliver real-world impact.
- Company: Leading AI firm focused on deploying scalable systems.
- Benefits: Competitive daily rate, hybrid work model, and high ownership opportunities.
- Other info: Fast-paced environment with significant career growth potential.
- Why this job: Join a dynamic team shaping the future of AI in real-world applications.
- Qualifications: Strong Python skills and experience with LLM-powered systems.
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
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 employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI agents. We want to see real-world applications of your work, so make sure to highlight any measurable impact you've made.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with deploying AI systems. We recommend practicing common technical questions and scenarios related to production environments.
✨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
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 deliver real-world AI solutions.
Showcase Your Projects: Include specific examples of AI systems you've built or contributed to. We want to see evidence of your work in production environments, so don’t hold back on the details!
Be Clear and Concise: When writing your application, keep it clear and to the point. Use straightforward language to describe your skills and experiences, making it easy for us to see how you fit into our team.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get the best chance to shine in front of our hiring team!
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 LLMs. 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 deployed AI systems into production. Highlight the measurable impact these projects had, focusing on how you owned them from concept to deployment. This will show your potential employer that you can deliver value, not just experiment.
✨Understand the Business Impact
Be ready to talk about how your work contributes to business outcomes. Think about examples where your AI solutions improved efficiency or decision-making processes. This shows that you’re not just a techie but someone who understands the bigger picture.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s AI initiatives and their approach to scaling AI systems. This not only demonstrates your interest but also gives you a chance to assess if the role aligns with your career goals and values.