Machine Learning Engineer

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING)OverviewDarwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of complex, data-driven marketing solutions, we help CMOs and marketing leaders drive measurable performance through advanced analytics, automation, and AI-powered insights.LocationChicago, IL area (Oak Brook / West Suburbs)Hybrid work model with 1–2 days onsite per week requiredReports ToVP of Data Engineering & Data ScienceResponsibilities / Essential FunctionsData Engineering & Platform FoundationsDesign, build, and maintain ETL pipelines in Databricks using Spark and Delta LakeIndependently implement data transformations, joins, and aggregations across large, multi-source datasetsBuild and maintain data validation and quality checks to ensure reliability of downstream analytics and ML workflowsOptimize Databricks jobs for performance, scalability, and cost efficiencyWrite and maintain clear technical documentation for data pipelines and tablesML Engineering & MLOpsPartner closely with Data Scientists to support traditional ML model development, including feature engineering, training, validation, and deploymentProductionize propensity, ranking, and segmentation models used in large-scale marketing campaignsBuild and maintain repeatable ML pipelines for training, batch scoring, and inferenceImplement model versioning, experiment tracking, and reproducibility standardsSupport model performance monitoring, drift detection, and retraining cyclesDeployment, Monitoring & OperationsDeploy data pipelines and ML workflows into production environments serving millions of recordsImplement monitoring and alerting for data and ML pipelinesSupport A/B testing and model performance evaluation in partnership with Data ScienceTroubleshoot production issues independently and collaborate effectively when escalation is neededGenAI (Secondary / Directional)Contribute to GenAI initiatives as capacity allowsStay informed on emerging AI technologies and tooling(GenAI is not the primary focus of this role today.)Required QualificationsExperience3–6 years of professional experience in machine learning engineering, data engineering, or a closely related roleExperience working in production environments with minimal day-to-day supervisionDemonstrated ability to collaborate effectively with Data Scientists and translate models into production systemsTechnical Skills (Must-Have)Data Engineering & PlatformApache Spark (PySpark, SparkSQL)Databricks (ETL pipelines, workflows, Delta Lake)Strong SQL skills (complex queries, joins, window functions, optimization)Experience building and maintaining scalable data pipelinesProgramming & Machine LearningPython (pandas, numpy, scikit-learn; experience with XGBoost or LightGBM preferred)Feature engineering and data preparation for ML modelsWorking knowledge of supervised learning models (classification, regression, ranking)MLOps & ProductionExperience deploying ML models into productionModel versioning and experiment tracking (e.g., MLflow or similar)Monitoring data quality and model performance in productionSupporting retraining and validation workflowsCloud & ToolingExperience with a major cloud platform (Databrick, AWS)Familiarity with workflow orchestration tools (Databricks Workflows or similar)Preferred Qualifications (Nice-to-Have)Experience with propensity modeling, customer segmentation, or marketing analyticsExposure to CI/CD concepts for data and ML pipelinesExperience with Docker or containerized deploymentsExposure to GenAI, LLMs, or RAG-based systemsMaster's degree in Computer Science, Statistics, or a related field#J-18808-Ljbffr