Machine learning engineer
Machine learning engineers take models built by data scientists, or bought from suppliers, and make them work reliably in live health and care services. They build the pipelines that train, test, deploy, and monitor models, and integrate them with clinical and operational systems. They make sure models stay safe, secure, fair, and effective once real patients and staff rely on them.
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- UK GDaD PCF role
- Machine learning engineer (Data)
- ESCO occupations
- artificial intelligence engineer (ISCO-08 2511)
- On GitHub
- Role document · Role data
Levels
| Band | Role level | UK GDaD PCF level | Civil Service grades | Job evaluation points |
|---|---|---|---|---|
| 7 | Senior machine learning engineer | Senior machine learning engineer | SEO/G7/G6 | 495 |
| 8b | Lead machine learning engineer | Lead machine learning engineer | G7/G6 | 595 |
In a digital health care organization
- Models in live use can drift as patient populations, clinical practice, or recording habits change, so performance is monitored for each patient group and models are retrained or withdrawn when needed.
- A model that informs diagnosis, triage, or treatment may be regulated as a medical device, which brings formal requirements for its software life cycle, change control, and post-market surveillance.
- Clinical safety cases need evidence about how a model fails, how its outputs are shown to clinicians, and what happens when it is unavailable.
- Models trained on patient data can leak information about individuals, so training data, model artifacts, and logs are protected like the data itself.
- Generative AI and large language models bring further risks, such as made-up content in clinical notes, which need specific testing and human oversight.
UK GDaD PCF role description
A machine learning engineer develops, assures and maintains machine learning models so they can be used in products and services.
In this role, you will:
- be responsible for the software development and technical infrastructure needed to design, train, deploy and scale machine learning models
- provide and maintain effective, secure and sustainable machine learning models for use in products and services
- support all stages of the machine learning life cycle
- help product teams evaluate and choose appropriate machine learning solutions
ESCO occupations and skills
ESCO lists the skills and knowledge that employers usually need (essential) or may need (optional) for each occupation. Use them as a checklist for development conversations.
artificial intelligence engineer
Artificial intelligence engineer apply methods of artificial intelligence in engineering, robotics and computer science to design programs which simulate intelligence including thinking models, cognitive and knowledge-based systems, problem solving, and decision making. They also integrate structured knowledge into computer systems (ontologies, knowledge bases) in order to solve complex problems normally requiring a high level of human expertise or artificial intelligence methods.
Essential skills and knowledge (31)
Skills and competences
- analyse big data
- analyse business requirements
- apply ICT systems theory
- create data sets
- creatively use digital technologies
- define technical requirements
- deliver visual presentation of data
- design process
- develop creative ideas
- develop statistical software
- use data processing techniques
Knowledge
- Python (computer programming)
- algorithms
- artificial neural networks
- business process modelling
- computer programming
- computer simulation
- data mining
- data models
- data science
- information architecture
- information categorisation
- information extraction
- information structure
- principles of artificial intelligence
- resource description framework query language
- systems development life-cycle
- task algorithmisation
- unstructured data
- visual presentation techniques
Other
Optional skills and knowledge (58)
Skills and competences
- apply systemic design thinking
- assess ICT knowledge
- build business relationships
- build predictive models
- build recommender systems
- design application interfaces
- design database scheme
- identify processes for re-engineering
- manage ICT data classification
- manage ICT semantic integration
- manage business knowledge
- perform dimensionality reduction
- utilise machine learning
Knowledge
- Agile project management
- Assembly (computer programming)
- C#
- C++
- COBOL
- CoffeeScript
- Common Lisp
- Erlang
- Groovy
- Haskell
- ICT project management methodologies
- Java (computer programming)
- JavaScript
- LINQ
- Lisp
- MATLAB
- ML (computer programming)
- Microsoft Visual C++
- N1QL
- Objective-C
- OpenEdge Advanced Business Language
- PHP
- Pascal (computer programming)
- Perl
- Process-based management
- Prolog (computer programming)
- R
- Ruby (computer programming)
- SAP R3
- SAS language
- SPARQL
- Scala
- Scratch (computer programming)
- Smalltalk (computer programming)
- Swift (computer programming)
- TypeScript
- VBScript
- Visual Basic
- business analytics
- business intelligence
- computer vision
- database development tools
- deep learning
- lean project management
- operational research
Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.