- Company Name
- Darwill
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:** Machine Learning Engineer (MLOps)
**Role Summary:**
Mid‑level engineer responsible for designing, building, and maintaining scalable data pipelines and production‑grade machine‑learning workflows. Collaborates with data scientists to operationalize propensity, ranking, and segmentation models, ensuring performance, reliability, and cost efficiency in a cloud‑based environment.
**Expectations:**
- 3–6 years of professional experience in machine learning engineering or data engineering.
- Ability to work independently with minimal supervision while delivering high‑quality code and documentation.
- Strong collaboration skills to translate data‑science prototypes into production systems.
- Commitment to continuous learning of emerging AI/ML tools and best practices.
**Key Responsibilities:**
- Design, develop, and optimize ETL pipelines in Databricks using Spark and Delta Lake.
- Implement data validation, quality checks, and performance tuning for large, multi‑source datasets.
- Partner with data scientists to feature‑engineer, train, validate, and deploy ML models.
- Build repeatable ML pipelines for batch scoring, inference, and model retraining.
- Manage model versioning, experiment tracking, and reproducibility (e.g., MLflow).
- Deploy and monitor data and ML workflows in production, handling alerts and issue resolution.
- Support A/B testing, model drift detection, and performance evaluation.
- Contribute to GenAI initiatives and stay updated on AI advancements (secondary focus).
**Required Skills:**
- **Data Engineering:** Apache Spark (PySpark, SparkSQL), Databricks, Delta Lake, advanced SQL (joins, window functions, optimization).
- **Programming & ML:** Python (pandas, numpy, scikit‑learn), experience with XGBoost or LightGBM, feature engineering, supervised learning (classification, regression, ranking).
- **MLOps:** Model deployment, versioning, experiment tracking (MLflow or similar), monitoring of data quality and model performance, retraining workflows.
- **Cloud & Tooling:** Major cloud platform (Databricks, AWS), workflow orchestration (Databricks Workflows or equivalents).
- **Additional:** Familiarity with CI/CD concepts for data/ML pipelines, Docker or containerization (nice‑to‑have).
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
- Master’s degree (preferred) in Computer Science, Statistics, or related discipline.
- Relevant certifications (e.g., Databricks Lakehouse, AWS Certified Solutions Architect) are advantageous but not mandatory.
Oakbrook terrace, United states
Hybrid
Junior
23-02-2026