Machine Learning Engineer Job at Darwill, Inc. in chicago

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.

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

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