> Markdown version of [/jobs/ext/3907290-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/3907290-ai-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). --- # AI-ML Engineer - **Company:** Siemens Plc - **Location:** UK - **Experience:** Experienced - **Salary:** £51,809.0 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Databases, Data Cleansing, Extract Transform Load (ETL), Data Presentation, Data Visualization, Django Web Framework, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Tensorflow, SQL Databases, Web Application Frameworks, Cloud Platform System, Feature Engineering, Pytorch, Flask (Web Framework), Generative AI, Fastapi, Pandas, Matplotlib, Scikit Learn, Information Technology, Machine Learning Operations, Data Pipelines - **Published:** October 25, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5210482862 ## About the Role Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, or any other quantitative field. 4 to 7 years of relevant experience in machine learning. Essential Skills: Proficiency in Python. Strong working knowledge of Numpy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch. Willingness to acquire technical skills and domain knowledge as required. Ability to execute end-to-end AI solution workflows, from problem definition to implementation. Expertise in exploratory analysis and presenting insights and statistics to stakeholders. Ability to select appropriate visualizations to effectively communicate data insights. Applied statistics skills and ability to derive value from data using statistical techniques. Experience with ETL processes involving different data types (timeseries, images, etc.) and sources. Proficiency in querying databases using SQL. Conceptual understanding of Machine Learning and Deep Learning algorithms. Ability to translate algorithm outputs to the corresponding problem domains. Practical experience in developing end-to-end Machine Learning workflows, including data preparation, feature engineering, exploratory analysis, algorithm selection, hyper-parameter tuning, validation, testing, and model management. Experience in computer vision and Natural Language Processing. Familiarity with cloud computing platforms such as Azure or AWS. Experience with MLOps tools or platforms. Basic understanding of the power and energy domain. Preferred Skills: Familiarity with BI tools and concepts of Graph DB. Experience in designing and implementing robust end-to-end modern data pipelines. Experience with AWS Sagemaker. Familiarity with web frameworks such as Flask, FastAPI, or Django. Knowledge of Generative AI. ## Description Analyze customer use-cases to determine the suitability of learning algorithms. Conduct thorough exploratory analysis and ensure data adequacy for ML projects.Utilize a variety of visualization tools to effectively communicate analysis and prediction results to stakeholders. Design and develop ML-based solutions, including model training, testing, and deployment into production environments. Ensure that all deliverables meet the organization's and project's quality requirements. Continuously review the performance of deployed models and implement necessary modifications for improvement. Guide and mentor team members on various aspects of data science project execution.