At a Glance
- Tasks: Design and deploy scalable AI/ML solutions in a leading Energy Trading company.
- Company: Join a world-leading Energy Trading & Commodities company with an innovative GenAI Team.
- Benefits: Competitive salary, flexible working options, and opportunities for continuous learning.
- Other info: Collaborate with diverse teams in a dynamic, agile environment focused on innovation.
- Why this job: Make a real impact by developing cutting-edge AI/ML systems that drive business success.
- Qualifications: 10+ years in software development and machine learning engineering required.
The predicted salary is between 70000 - 90000 £ per year.
This is an exciting role for a senior AI/ML Research Engineer to join a world-leading Energy Trading & Commodities company as part of their highly innovative GenAI Team. You will be responsible for designing, developing, and deploying scalable AI/ML solutions across multiple business areas. The role focuses on delivering production-grade machine learning systems while ensuring alignment with enterprise quality, reliability and scalability standards. You will lead end-to-end AI/ML solution development from data exploration and experimentation through to deployment, monitoring and continuous optimisation. You will work closely with cross-functional teams including data engineering, infrastructure, DevOps, and product management to translate business needs into robust, reusable machine learning architectures.
Required Qualifications & Experience
- Bachelor’s, Master’s, or PhD in a relevant discipline (Engineering, Computer Science, Statistics, or related fields)
- 10+ years of experience in software development and machine learning engineering
Core Technical Skills
- Strong expertise in designing large-scale machine learning systems and architectures
- Advanced programming skills (Python preferred) with experience in frameworks and tools such as JavaScript, Kafka, and reactive systems
- Extensive experience with cloud-based development, particularly on Azure, including AI/ML services and data platforms
- Proven experience with Kubernetes for application deployment, scaling, and monitoring
- Strong background in CI/CD pipeline design, automation, and maintenance
- Hands‑on experience with data engineering tools and storage solutions (e.g., ADLS, Spark, Databricks, SQL/NoSQL databases)
- Experience with distributed computing and big data processing frameworks such as PySpark
- Knowledge of infrastructure‑as‑code tools such as Terraform and Helm
Advanced / Specialist Expertise
- Experience building and deploying GenAI solutions using frameworks such as LangChain and Azure OpenAI
- Development of enterprise‑grade RAG (Retrieval-Augmented Generation) systems, including context engineering and multimodal data pipelines
- Design and deployment of autonomous multi‑agent systems using modern orchestration frameworks and evaluation approaches
- Experience delivering Text-to-SQL solutions and natural language interfaces for structured data environments
Additional Skills
- Strong understanding of data processing, cleansing, and handling large structured and unstructured datasets
- Solid foundation in Linux, scripting (Bash/PowerShell), and networking fundamentals
- Excellent communication skills with the ability to translate complex technical concepts into business terms
- Experience working in agile, cross‑functional, and globally distributed teams
- Continuous learning mindset with a focus on emerging technologies and innovation
Nice-to-Have
- Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI)
- Front‑end development (React) or backend development (.NET/C#)
- Commercial awareness and understanding of business value delivery
Senior AI/ML Research Engineer in London employer: Eaglecliff
Join a world-leading Energy Trading & Commodities company that champions innovation and excellence in the AI/ML space. As part of the GenAI Team, you'll thrive in a collaborative work culture that prioritises continuous learning and professional growth, while enjoying the unique advantage of working at the forefront of energy technology. With a commitment to employee development and a focus on delivering impactful solutions, this role offers a meaningful opportunity to shape the future of AI in a dynamic industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI/ML Research Engineer in London
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We think you need these skills to ace Senior AI/ML Research Engineer in London
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 Eaglecliff, 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 Eaglecliff. 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 Eaglecliff
✨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!
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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 Eaglecliff!
✨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.
