Machine Learning Engineer
Core
Bridge data science experimentation and robust software engineering to productionise, scale, and maintain advanced analytical and predictive models.
Role type
Machine Learning Engineer (MLOps & Distributed Computing)
Builds
Production-grade data processing and model training pipelines, automated ML lifecycles, and scalable R-based modeling workflows.
Domain
IT Consultancy, Public & Private sectors, Data Engineering, Statistical Modeling
Deliverable
production ML models
Required skills
Software engineering fundamentals, Databricks ecosystem, Apache Spark internals, R for production, Delta Lake, CI/CD pipelines (via careerplan.io/jobs/38d45904-f487-40b6-8c0c-c43c72076312-machine-learning-engineer-at-bailey-abbott)
Preferred skills
Python/PySpark, Cloud infrastructure (Azure/AWS/GCP), Infrastructure as Code (Terraform), Model drift detection
Technologies
Databricks, Apache Spark, MLflow, Unity Catalog, Sparklyr, SparkR, Delta Lake, Git, Terraform
Responsibilities
Build and optimize production-grade data processing and model training pipelines in Apache Spark and Databricks; Standardize model deployment, versioning, monitoring, and automated retraining workflows; Apply software engineering best practices to analytical codebases; Optimize and scale R-based modeling workflows over distributed data architectures; Partner with data architects and engineers to integrate predictive outputs; Monitor pipeline latency and manage compute cluster sizing.
Seniority
Mid-Senior, hands-on IC
