Machine Learning Engineer

Darwill, Inc.•Oakbrook Terrace, IL

just now•Hybrid

About The Position

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) 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..

Requirements

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
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
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)
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
Experience with a major cloud platform (Databrick, AWS)
Familiarity with workflow orchestration tools (Databricks Workflows or similar)

Nice To Haves

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

Responsibilities

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
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
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
Contribute to GenAI initiatives as capacity allows
Stay informed on emerging AI technologies and tooling