> Markdown version of [/jobs/ext/3520435-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3520435-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Engineer

- **Company:** Adsp
- **Location:** London, UK (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Transformation, Data Mining, Python (Programming Language), Machine Learning, NumPy, SQL Databases, Tableau (Software), Jupyter, Git, Pandas, Scikit Learn, Xgboost, Docker
- **Published:** October 1, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/ml-engineer/49644133

## About the Role

We are looking for a Machine Learning Engineerwhoenjoys seeing their work used as part of 'real-life' solutions. Not only will your work directly contribute to our client deliverables, but you will have the opportunity to see the process through from solution design to deployment, working in close collaboration with our wider team.In short, you'll form an integral part of our close-knit team and will have the opportunity to directly contribute to the continued success of the business. We're looking for someone with a co-operative, can-do attitude who can build high-quality data science solutions.KEY RESPONSIBILITIES:Participate in the design, development, testing, and evaluation of data science solutionsTrain and deploy state-of-the-art machine learning modelsBuild processes for extracting, cleaning and transforming data (SQL / Python)Ad-hoc data mining for insights using Python + Jupyter notebooksPresent insights and predictions in live dashboards using Tableau / PowerBISupport the
presentation of findings to clients through written documentation, calls and presentationsAdopt data science best-practice e.g. Git / Docker / cloud deploymentKEY REQUIREMENTS:Experience with applied machine learning techniques in Python (e.g. xgboost, regression, decision trees)Experience of working collaboratively as part of a data science team, using tools like Git to adhere to established data science and AI best practicesExperience of using different analysis techniques to draw insight from data, using tools such as Python and SQLStrong foundation in mathematics and statisticsProficiency in Python, including relevant libraries for data analysis and machine learning (e.g. sklearn, Pandas, NumPy)Knowledge of data analysis techniques, such as statistical modelling and visualisation, to draw insight from dataFamiliarity with Git as part of the development processEffective organisational skills e.g. planning, time managementEffective problem-solving and analytical skillsHigh attention to