Hybrid Applied ML Engineer for Real-World AI Impact

The Opportunity Hub UK Ltd; Town of Cambridge; Hybrid; AUD 95,000 - 148,000; Full time

Benefits offered by this job

Private medical insurance

Pension contribution

Bonus scheme

Generous annual leave

Hybrid working

Job summary

The Opportunity Hub UK Ltd in Cambridge is seeking an ambitious Applied Machine Learning Engineer to move models from notebook to production and ensure post-deployment impact. The role sits at the heart of a growing SME building practical ML solutions for real operations.

You will design, train and deploy models, build scalable data pipelines, and collaborate with product and engineering teams to translate requirements into working systems, while monitoring performance and adjusting as needed.

Qualifications

• Experience building and shipping ML models in Python (PyTorch or TensorFlow).
• Lifecycle covered: data wrangling, training, evaluation, deployment, monitoring.
• Knowledge of MLOps: model versioning and reproducible pipelines.
• Ability to explain technical trade-offs to non-technical stakeholders.
• Capable of independent work while asking the right questions.

Responsibilities

• Design, train and deploy ML models to solve business problems.
• Build and maintain data pipelines feeding models at scale.
• Collaborate with product and engineering to translate requirements into working systems.
• Monitor model performance in production and retrain as needed.
• Write clean, testable code and document decisions for future teams.
• Contribute to architecture, tooling and AI practice directions.

Skills

Python

PyTorch

TensorFlow

Data wrangling

MLOps

Model deployment

Explain trade-offs

Independent work

Curiosity about AI

Tools

Git

DVC

CI/CD for ML

Job description

The Opportunity Hub UK Ltd in Cambridge is seeking an ambitious Applied Machine Learning Engineer to move models from notebook to production and ensure post-deployment impact. The role sits at the heart of a growing SME building practical ML solutions for real operations.

You will design, train and deploy models, build scalable data pipelines, and collaborate with product and engineering teams to translate requirements into working systems, while monitoring performance and adjusting as needed.