Senior MLOps/Data Engineer

What you'll do

In a nutshell, own the end-to-end ML lifecycle on Azure and Databricks, working with applied scientists to operate reliable models.

Orchestrate and maintain ML pipelines (ingest feature engineering train evaluate deploy monitor

repeat) on Azure + Databricks

Standardize experimentation using MLflow or similar tools (tracking, artifacts, model registry, stages)

Automate jobs with Databricks Workflows and CI/CD (GitHub Actions or Azure DevOps)

Implement data & model observability: freshness/completeness, drift (features/model), training/serving skew, SLA/SLO monitoring

Ensure security & compliance

Handle incidents and post-mortems for ML pipelines and serving infrastructure

What you'll need

Excellence in Python software engineering and developing tests

Fundamental understanding of Machine Learning

3+ years in Data Eng/MLOps roles

Strong PySpark

Hands-on with Databricks and Delta Lake

CI/CD for data/ML (Git, PR workflow, automated tests, environment pinning)

Azure basics

Monitoring and building dashboards

Clear communication; operational-excellence mindset (SLA/SLO ownership)

What's nice to have

Unity Catalog experience

Databricks Feature Store

Terraform for workspace/clusters/jobs/UC objects

Telemetry domain exposure

Optimize PySpark jobs (partitioning, caching, etc.) and cost (autoscaling, spot).

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.