Artificial Intelligence Engineer ,vertex ai,gcp

GCP expertise (Cloud Run, Cloud Build, Vertex AI, Big Query, IAM)

Deploying and managing web/API services

Experience with LangGraph or similar agent frameworks

LLM evaluation and assessment

MLOps and AI platform operations

Nice to Have (Not Critical)

1 Data Discovery and Preparation

Explore available datasets and assess their relevance completeness quality and suitability for modelling

Identify dataquality limitations missing information potential bias and other factors affecting analytical validity

Develop reproducible datacleaning transformation and featureengineering logic

Work with Dyson data and domain teams to clarify data definitions assumptions and dependencies

Ensure that data preparation follows Dyson security privacy and governance requirements

2 Predictive Modelling

Develop evaluate and improve predictive forecasting and recommendation models for assigned use cases

Select modelling approaches appropriate to the available data required outputs and operational constraints

Establish suitable baseline approaches against which model improvements can be assessed

Conduct structured experimentation model comparison and parameter optimisation

Clearly document modelling assumptions design decisions and known limitations

3 Model Evaluation and Quality Assurance

Define appropriate validation strategies evaluation datasets and performance measures

Test model performance across relevant data segments scenarios and time periods

Assess model reliability uncertainty stability and sensitivity to changes in input data

Analyse model failures and distinguish modelling issues from dataquality or evaluationdata issues

Produce traceable evaluation evidence sufficient for Dysons technical and business acceptance reviews

4 Scalable Data Science Workflows

Develop reproducible datapreparation modeltraining inference and scoring workflows using Dysonapproved cloud tooling

Structure code and configuration to support repeatable experimentation testing and future maintenance

Collaborate with Dyson engineering teams to prepare model components for integration into downstream platforms

Support implementation of appropriate model monitoring performance tracking and retraining processes

Ensure implementation follows Dyson coding versioncontrol peerreview and maintainability standards

5 Documentation Communication and Handover

Maintain clear technical documentation covering data preparation features modelling methods evaluation and limitations

Communicate analytical findings and recommendations effectively to technical and nontechnical stakeholders

Maintain sourcecontrolled notebooks code configuration and supporting artefacts

Participate in technical reviews stakeholder playback sessions and knowledgesharing activities

Conduct knowledge transfer to Dyson data scientists and engineers where required