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# Machine Learning Engineer

- **Company:** Understanding Recruitment
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Distributed Systems, Django Web Framework, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Performance Tuning, Tensorflow, Software Engineering, Pytorch, Flask (Web Framework), Large Language Models, Generative AI, Backend, Fastapi, Containerization, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Api Design, Docker
- **Published:** October 30, 2026
- **Apply:** https://www.understandingrecruitment.com/job/machine-learning-engineer-9284/

## About the Role

We're working with an exciting technology company looking to hire a Machine Learning Engineer with a strong Python software engineering background to help build, deploy, and scale production-grade AI and machine learning systems.

This isn't a traditional Data Science or research-focused position. We're looking for someone who enjoys taking machine learning models beyond experimentation and turning them into reliable, scalable, real-world applications.

The ideal candidate will combine hands-on machine learning experience with strong software engineering fundamentals, particularly across Python development, backend systems, APIs, testing, deployment, and production infrastructure., * Strong commercial experience developing production software using Python.

 * Experience working as a Machine Learning Engineer, Software Engineer, AI Engineer, or in a similar engineering-focused role.
 * Solid understanding of software engineering principles, including object-oriented programming, system design, testing, and maintainable code.
 * Hands-on experience building APIs and backend services using technologies such as FastAPI, Flask, or Django.
 * Experience deploying, maintaining, and monitoring machine learning models in production.
 * Familiarity with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
 * Experience with cloud platforms such as AWS, GCP, or Azure.
 * Understanding of containerisation, CI/CD pipelines, and modern deployment practices.
 * A genuine engineering mindset, with a focus on building robust, scalable, and maintainable systems.

Nice to Have

 * Experience with Docker, Kubernetes, and cloud-native architectures.
 * Exposure to MLOps tooling, model serving, and ML infrastructure.
 * Experience building production applications using LLMs or generative AI.
 * Knowledge of distributed systems, performance optimisation, and infrastructure automation.

## Description

Machine Learning Engineer - Python & Production AI

We're working with an exciting technology company looking to hire a Machine Learning Engineer with a strong Python software engineering background to help build, deploy, and scale production-grade AI and machine learning systems.

This isn't a traditional Data Science or research-focused position. We're looking for someone who enjoys taking machine learning models beyond experimentation and turning them into reliable, scalable, real-world applications.

The ideal candidate will combine hands-on machine learning experience with strong software engineering fundamentals, particularly across Python development, backend systems, APIs, testing, deployment, and production infrastructure.

What We're Looking For

 * Strong commercial experience developing production software using Python.
 * Experience working as a Machine Learning Engineer, Software Engineer, AI Engineer, or in a similar engineering-focused role.
 * Solid understanding of software engineering principles, including object-oriented programming, system design, testing, and maintainable code.
 * Hands-on experience building APIs and backend services using technologies such as FastAPI, Flask, or Django.
 * Experience deploying, maintaining, and monitoring machine learning models in production.
 * Familiarity with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
 * Experience with cloud platforms such as AWS, GCP, or Azure.
 * Understanding of containerisation, CI/CD pipelines, and modern deployment practices.
 * A genuine engineering mindset, with a focus on building robust, scalable, and maintainable systems.

Nice to Have

 * Experience with Docker, Kubernetes, and cloud-native architectures.
 * Exposure to MLOps tooling, model serving, and ML infrastructure.
 * Experience building production applications using LLMs or generative AI.
 * Knowledge of distributed systems, performance optimisation, and infrastructure automation.