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- Tasks: Design and build AI agents and workflows that deliver 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.
- Why this job: Make a difference by working on live AI systems at scale.
- 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
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 industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI systems. We want to see your work in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to machine learning. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talent, and applying directly can give you a better chance of standing out. Plus, it shows you’re genuinely interested in joining our team.
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 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 Your Projects: If you’ve worked on any AI projects, make sure to include them in your application. We love seeing real-world examples of your work, especially those that have delivered measurable impact. This is your opportunity to show us what you can do!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, it helps us keep track of your application better!
How to prepare for a job interview at Russell Tobin
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the frameworks mentioned in the job description, like LangChain and RAG systems. Brush up on your experience with deploying LLM-powered systems, as you’ll want to showcase your hands-on skills during the interview.
✨Showcase Real-World Impact
Prepare examples of AI systems you've built that are actively used and delivering measurable value. Be ready to discuss how your projects have made a difference in previous roles, especially in fast-paced environments where speed and pragmatism were key.
✨Understand the Business Side
Since this role focuses on delivering business impact, think about how your technical skills can translate into real-world applications. Be prepared to discuss how you balance model performance, latency, and cost efficiency in your past projects.
✨Collaborate and Communicate
Highlight your experience working closely with engineering teams and integrating AI systems into products and workflows. Good communication is crucial, so practice explaining complex concepts in a way that’s easy to understand for non-technical stakeholders.