Machine Learning Engineer

Overview Darwill 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. We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional machine learning models (e.g., propensity and segmentation models) while also building and maintaining the core data pipelines on Databricks that power our analytics and modeling platforms. This role is intentionally scoped for a mid-level engineer: someone with enough experience to work independently and make sound engineering decisions, but who is still hands‑on, execution-focused, and eager to grow. This is not an entry‑level position, and it is not a principal or architect-level role. Location Chicago, IL area (Oak Brook / West Suburbs) Hybrid work model with 1--2 days onsite per week required Reports To VP of Data Engineering & Data Science Responsibilities / Essential Functions Data Engineering & Platform Foundations Design, build, and maintain ETL pipelines in Databricks using Spark and Delta Lake Independently implement data transformations, joins, and aggregations across large, multi-source datasets Build and maintain data validation and quality checks to ensure reliability of downstream analytics and ML workflows Optimize Databricks jobs for performance, scalability, and cost efficiency Write and maintain clear technical documentation for data pipelines and tables ML Engineering & MLOps Partner closely with Data Scientists to support traditional ML model development, including feature engineering, training, validation, and deployment Productionize propensity, ranking, and segmentation models used in large-scale marketing campaigns Build and maintain repeatable ML pipelines for training, batch scoring, and inference Implement model versioning, experiment tracking, and reproducibility standards Support model performance monitoring, drift detection, and retraining cycles Deployment, Monitoring & Operations Deploy data pipelines and ML workflows into production environments serving millions of records Implement monitoring and alerting for data and ML pipelines Support A/B testing and model performance evaluation in partnership with Data Science Troubleshoot production issues independently and collaborate effectively when escalation is needed GenAI (Secondary / Directional) Contribute to GenAI initiatives as capacity allows Stay informed on emerging AI technologies and tooling (GenAI is not the primary focus of this role today.) Required Qualifications Experience 3--6 years of professional experience in machine learning engineering, data engineering, or a closely related role Experience working in production environments with minimal day-to-day supervision Demonstrated ability to collaborate effectively with Data Scientists and translate models into production systems Technical Skills (Must-Have) Data Engineering & Platform Apache Spark (PySpark, SparkSQL) Databricks (ETL pipelines, workflows, Delta Lake) Strong SQL skills (complex queries, joins, window functions, optimization) Experience building and maintaining scalable data pipelines Programming & Machine Learning Python (pandas, numpy, scikit-learn; experience with XGBoost or LightGBM preferred) Feature engineering and data preparation for ML models Working knowledge of supervised learning models (classification, regression, ranking) MLOps & Production Experience deploying ML models into production Model versioning and experiment tracking (e.g., MLflow or similar) Monitoring data quality and model performance in production Supporting retraining and validation workflows Cloud & Tooling Experience 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 analytics Exposure to CI/CD concepts for data and ML pipelines Experience with Docker or containerized deployments Exposure to GenAI, LLMs, or RAG-based systems Master's degree in Computer Science, Statistics, or a related field #J-18808-Ljbffr