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# Data Scientist - ML Engineer

- **Company:** Engaging Networks
- **Location:** London, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Databases, Continuous Delivery, Information Engineering, Data Structures, DevOps, Python (Programming Language), Machine Learning, NumPy, Performance Tuning, Tensorflow, Azure Machine Learning, SciPy, SQL Databases, Pytorch, Delivery Pipeline, Large Language Models, Deep Learning, Generative AI, Pandas, Build Management, AI Platforms, Scikit Learn, Machine Learning Operations, Data Pipelines
- **Published:** May 26, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/x/37375241/

## About the Role

Professional Experience: 2+ years of industry experience as a Data Scientist or ML Engineer, with a track record of moving real projects into production.
Python Mastery: Expert-level Python development skills, including standard libraries like NumPy, Pandas, and SciPy.
Predictive & Deep Learning: Deep knowledge of machine learning workflows in scikit-learn and at least one deep learning framework such as PyTorch or TensorFlow.
Generative AI: Practical experience with LLM orchestration (e.g., LangChain) and vector databases for RAG workflows.
Cloud & DevOps: Professional experience with AWS/SageMaker (or similar) and a strong comfort level with Linux/Unix environments.
Data Engineering: Proficiency in SQL and experience preparing or scraping complex datasets for machine learning.
Education: A strong background in Statistics, Mathematics, or Physical Sciences.
Soft Skills: A meticulous attention to detail combined with the "founding hire" mindset-curious, eager to build from scratch, and interested in making change happen.
Autonomy: Shape the AI culture and tech stack of an established company from day one.
Flexibility: Flexible working hours and remote-friendly locations.
Growth: Budget to attend major sector conferences and stay at the cutting edge of AI/ML.

## Description

Architecture & Deployment: Lead the end-to-end development and testing of a Python-based ML platform.
Cloud Infrastructure: Architect and manage ML environments specifically within AWS (SageMaker, Lambda, S3), ensuring scalable training and deployment pipelines.
Data Orchestration: Develop a unified data structure for our AI platform using existing and new APIs and databases.
Hybrid Modeling: Build and deploy classifier and predictive models(e.g., churn prediction, propensity scoring) using scikit-learn or similar. Implement modern LLM applications, including Retrieval-Augmented Generation (RAG) and prompt engineering.
Strategic Leadership: Collaborate with product and engineering leaders to identify high-impact AI opportunities and make the vision a reality.
Performance Optimization: Continuous deployment and evaluation of the platform to improve accuracy, efficacy, and prediction speed.


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