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8 hours ago
We are seeking a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have extensive experience in designing and building data pipelines, training models, and deploying them into production at scale.
Job DescriptionThe Machine Learning Engineer will be responsible for bridging the gap between data engineering and applied machine learning. They will work on transforming raw events into usable features, defining and computing labels from historical events, and developing and training ML models using Python.
Key Responsibilities:- Data Pipelines: Design and build data pipelines to transform raw events into usable features.
- Label Computing: Define and compute labels from historical events.
- ML Model Development: Develop and train ML models using Python (scikit-learn, LightGBM, XGBoost).
- Real-Time Inference: Build real-time inference services to serve predictions into production systems.
- Pipeline Setup: Set up retraining and monitoring pipelines (Airflow, MLflow, or similar).
- Integration: Collaborate with backend engineers to integrate model outputs into Gleantap workflows.
- Quality Control: Ensure data quality, reproducibility, and compliance.
- Experience: 3–5+ years of experience in data engineering or applied ML.
- Skills: Strong proficiency in Python, SQL, and one or more ML libraries.
- Data Pipelines: Experience with data pipelines (Airflow, dbt, or custom ETL).
- Event-Driven Systems: Comfortable with event-driven systems.
- 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).
- 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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