
AI-Driven Data Engineer
1 week ago
Job Title: Machine Learning Pipeline Developer
">We're looking for a skilled professional to join our team as a Machine Learning Pipeline Developer. This role is crucial in bridging the gap between data engineering and applied machine learning, building pipelines, training models, and deploying them into production at scale.
">- About Us:
- We're evolving into an AI-native platform, where intelligent agents predict churn, upsell opportunities, and automate member engagement.
- Data Pipelines: Design and build efficient data pipelines to transform raw events (visits, purchases, campaigns) into usable features.
- Label Generation: Define and compute labels (e.g., churn, upsell, lead quality) from historical events.
- Machine Learning Modeling: Develop and train ML models (e.g., churn prediction, upsell propensity, lead scoring) using Python (scikit-learn, LightGBM, XGBoost).
- Inference Services: Build real-time inference services to serve predictions into production systems.
- Retraining and Monitoring: Set up retraining and monitoring pipelines (Airflow, MLflow, or similar).
- Integration: Collaborate with backend engineers to integrate model outputs into Gleantap workflows.
- Data Quality: Ensure data quality, reproducibility, and compliance (HIPAA for healthcare customers).
- Experience: 3–5+ years of experience in data engineering or applied ML.
- Skills: Strong proficiency in Python, SQL, and one or more ML libraries (scikit-learn, LightGBM, XGBoost, PyTorch).
- Data Pipelines: Experience with data pipelines (Airflow, dbt, or custom ETL).
- Event-Driven Systems: Comfortable with event-driven systems (Kafka, Redis, ClickHouse or similar OLAP).
- ML Lifecycle: Understanding of ML lifecycle: training, serving, monitoring, retraining.
- Problem-Solving: Strong problem-solving skills and eagerness to work in a startup environment.
- MLOps Tools: MLOps tools (MLflow, BentoML, Ray Serve).
- Bandit Algorithms: Experience with bandit algorithms, A/B testing, or uplift modeling.
- Customer Engagement: Prior work with customer engagement, CRM, or subscription businesses.
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