ML Engineer – Azure Production ML & Pipelines

Gensler; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Disability benefits

Wellness programs

Flex spending

Paid holidays

Paid time off

401k

Profit sharing

Employee stock ownership

Job summary

Gensler seeks a seasoned Machine Learning Engineer to design and operate production ML systems across BIM, geospatial, and design data domains. You will own end-to-end ML lifecycles on Azure, build reliable data pipelines, and translate research into production-ready platforms.

You will collaborate with AI and data engineers, data scientists, designers, and product stakeholders to drive adoption, governance, and measurable improvements in project decision-making.

Qualifications

• 8+ years hands-on experience building and operating production ML systems on Azure.
• Strong Python and software engineering fundamentals with tests and docs.
• Experience with Azure ML, Databricks, Data Factory, storage, and MLflow.

Responsibilities

• Design and maintain Azure-based ETL/ELT pipelines delivering data to models and dashboards.
• Own production ML lifecycle on Azure: deployment, monitoring, retraining, rollback.
• Implement CI/CD and testing standards for ML workflows.
• Extend cloud data architecture to support analytics and ML workloads.
• Document datasets with owners and ensure provenance and governance.
• Translate research prototypes into production-ready systems.
• Work across BIM/IFC, geospatial, and other design data domains.

Skills

Azure ML

Python

MLOps

CI/CD

Data pipelines

Cloud architecture

Experiment tracking

Model deployment

Monitoring

collaboration

Education

Bachelor's or advanced degree in CS/SE/Data Science

Tools

Azure ML

Databricks

Azure Data Factory / Fabric

Blob/Data Lake storage

MLflow

Job description

Gensler seeks a seasoned Machine Learning Engineer to design and operate production ML systems across BIM, geospatial, and design data domains. You will own end-to-end ML lifecycles on Azure, build reliable data pipelines, and translate research into production-ready platforms.

You will collaborate with AI and data engineers, data scientists, designers, and product stakeholders to drive adoption, governance, and measurable improvements in project decision-making.