At a Glance
- Tasks: Design and develop AI agents using cutting-edge large language models.
- Company: Join a forward-thinking tech company focused on impactful AI solutions.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for growth.
- Other info: Collaborative environment with significant ownership and visibility in projects.
- Why this job: Make a real-world impact by building live AI systems that solve complex challenges.
- Qualifications: Strong Python skills and experience with LLM-powered applications required.
The predicted salary is between 70000 - 90000 £ per year.
Design and develop AI agents and agentic workflows powered by large language models (LLMs), combining retrieval-augmented generation (RAG), reasoning frameworks, and tool orchestration. Build intelligent multi-step systems that leverage planning, memory, and external tools to solve complex business and operational challenges. Develop and maintain MCP-based architectures (or equivalent orchestration frameworks) to enable structured context management, tool interoperability, and reliable agent execution. Contribute to AI-driven recommendation, classification, forecasting, and decision-support systems operating on large-scale, real-world datasets. Automate complex workflows and business processes through AI, delivering measurable improvements in efficiency, decision quality, and operational performance.
What You’ll Do
- Own AI initiatives end-to-end, from discovery and experimentation through production deployment, monitoring, and continuous optimisation.
- Design, build, and deploy production-grade AI agents that operate reliably at scale in real-world environments.
- Integrate AI capabilities into products, APIs, and business workflows, ensuring solutions are scalable, maintainable, and deliver clear business value.
- Collaborate closely with software engineers, platform teams, and stakeholders to build robust, observable, and resilient systems.
- Make pragmatic engineering decisions that balance model quality, latency, reliability, and cost efficiency.
Core Requirements
- Strong Python engineering skills with the ability to write clean, maintainable, production-quality code and apply sound software design principles.
- Proven experience deploying LLM-powered applications into production, with demonstrable examples of systems delivering real business value.
- Hands-on experience building AI agents and agentic workflows, including tool integration, orchestration, planning, and multi-step reasoning.
- Experience developing and deploying RAG architectures that move beyond proof-of-concept implementations and deliver measurable outcomes.
- Familiarity with MCP frameworks or equivalent orchestration patterns, including structured context management and tool integration (e.g., FastMCP, FastAPI, LangGraph, LangChain).
- Strong understanding of LLM capabilities, limitations, and trade-offs, with practical experience mitigating hallucinations, latency, reliability, and cost challenges.
- Experience deploying and operating systems in cloud environments such as AWS, GCP, or Azure using modern engineering and DevOps practices.
- Working knowledge of SQL and data manipulation techniques.
Ideal Profile
- Master’s degree or higher in Computer Science, Mathematics, Engineering, Data Science, Physics, or a related quantitative discipline.
- Demonstrated experience building, shipping, and iterating on production AI systems, with the ability to clearly articulate architectural and technical decisions.
- Strong sense of ownership and accountability, with a track record of driving initiatives independently and delivering outcomes.
- Product-minded approach, focused on solving business problems and creating impact rather than solely optimising model performance.
- Comfortable operating in fast-paced, ambiguous environments while maintaining high engineering standards.
- Collaborative team player who contributes positively to team culture, knowledge sharing, and continuous improvement.
- For Lead-level candidates, experience mentoring engineers and owning complex projects or workstreams from conception through delivery.
Strongly Preferred
- Experience building SaaS, B2B, or enterprise AI products.
- Background working in high-growth or scaling organisations where speed, execution, and pragmatism are critical.
- Evidence of production AI systems that are actively used by customers or internal stakeholders and delivering measurable value.
Why Join Us
- Build AI systems that are live in production and delivering real-world impact at scale.
- Join a strategic AI programme with strong executive sponsorship, investment, and long-term commitment.
- Enjoy significant ownership, autonomy, and visibility across both product and business initiatives.
- Help shape how AI is adopted and operationalised across a global organisation.
- Work alongside experienced engineers, product leaders, and AI practitioners solving meaningful business challenges.
Machine Learning Engineer employer: Eames Consulting
Join a forward-thinking company that empowers its employees to take ownership of AI initiatives, fostering a culture of innovation and collaboration. With a strong commitment to professional growth, you will have the opportunity to work on impactful projects that leverage cutting-edge technology in a dynamic environment. Enjoy the benefits of working alongside experienced professionals while contributing to meaningful solutions that drive real-world change.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Eames Consulting!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Machine Learning Engineer at Eames Consulting.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Eames Consulting.
✨Apply Directly through Our Website
When you find a suitable opening like Machine Learning Engineer at Eames Consulting, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Eames Consulting, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Eames Consulting. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Eames Consulting
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Eames Consulting!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
