MLOps Data Engineer (GCP) in City of London

We are looking for a Data Engineer with strong MLOps ownership, someone who builds reliable data pipelines and designs, runs, and improves ML pipelines in production. You won't be training models day-to-day like a Data Scientist; instead, you'll enable Data Science by delivering high-quality datasets, reproducible training pipelines, robust deployments, and monitoring that keeps ML systems healthy and trustworthy.

What you'll do:

• Design, build, and operate scalable data pipelines for ingestion, transformation, and distribution.
• Develop and maintain ML pipelines end-to-end: data preparation, feature generation, training orchestration, packaging, deployment, and retraining.
• Partner closely with Data Scientists to productionise models: standardise workflows, ensure reproducibility, and reduce time-to-production.
• Build and maintain MLOps automation: CI/CD for ML, environment management, artefact handling, versioning of data/models/code.
• Implement observability for ML systems: monitoring, alerting, logging, dashboards, and incident response for data + model health.
• Establish best practices for data quality and ML quality: validation checks, pipeline tests, lineage, documentation, and SLAs/SLOs.
• Optimise cost and performance across data processing and training workflows (e.g., Spark tuning, BigQuery optimisation, compute autoscaling).
• Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices.

What makes you a great fit:

• 4+ years of experience as a Data Engineer (or ML Platform / MLOps Engineer with strong DE foundations) shipping production pipelines.
• Strong Python and SQL skills; ability to write maintainable, testable, production-grade code.
• Solid understanding of MLOps fundamentals: model lifecycle, reproducibility, deployment patterns, and monitoring needs.
• Hands-on experience with orchestration and distributed processing in a cloud environment.
• Experience with data modelling and ETL/ELT patterns; ability to deliver analysis-ready datasets.
• Familiarity with containerization and deployment workflows (Docker, CI/CD, basic Kubernetes/serverless concepts).
• Strong GCP experience and services such as Vertex, BigQuery, Composer, Dataproc, Cloud Run, Dataplex, Cloud Storage/or at least one major cloud provider, GCP, AWS, Azure.
• Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments.

Nice to have / big advantage:

• Experience with ML tooling such as MLflow (tracking/registry), Vertex AI / SageMaker / Azure ML, or similar platforms.
• Experience building and maintaining feature stores (e.g., Feast, Vertex Feature Store).
• Experience with data/model validation tools (e.g., Great Expectations, TensorFlow Data Validation, Evidently).
• Knowledge of model monitoring concepts: drift, data quality issues, performance degradation, bias checks, and alerting strategies.
• Infrastructure-as-Code (Terraform) and secrets management / IAM best practices.
• Familiarity with governance/compliance standards and audit requirements.