MLOps Engineer
LenDenClub
Full Time5+ yearsPosted 11 days ago
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Overview
Position Type
Full Time
Experience
5+ years
Job Description
Profile Summary:
We are looking for a hands-on MLOps Engineer to own the end-to-end ML lifecycle — from data pipelines to production deployment, monitoring and retraining — with a focus on reliability, automation and cost efficiency.
Key Responsibilities
- Own the full ML lifecycle: data prep, feature pipelines, training, deployment, monitoring and automated retraining.
- Build and maintain CI/CD pipelines for ML with containerized deployments (Docker, Kubernetes).
- Design scalable data/ETL workflows (batch & streaming) using PySpark and a workflow orchestrator (e.g., Airflow).
- Set up a feature store and model registry for reproducible, governed model delivery.
- Implement model monitoring and drift detection (data drift, concept drift, performance decay) and trigger retraining.
- Build observability for model and API health — metrics, logs, alerts, dashboards.
- Optimize cloud/infrastructure costs (autoscaling, spot instances, right-sizing) and track cost per model.
- Enforce ML governance — versioning, lineage, audit logs, and compliance requirements.
Nice to Have
- Experience with streaming data (Kafka / Flink).
- Familiarity with feature stores (e.g., Feast).