Machine Learning Engineer

About the role

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