MLOps Engineer
5 days ago
Role Overview
We are seeking an experienced MLOps Engineer to architect, automate, and govern ML model operations across the entire lifecycle at scale.
Responsibilities:
- Manage full ML lifecycle: data prep, feature pipelines, training, deployment, monitoring and retraining automation and model governance.
- Design and implement CI/CD pipelines with Docker, Kubernetes/ECS,
Terraform/CloudFormation.
- Build scalable ETL/ELT workflows (batch & streaming) using PySpark, Airflow,
SQL tuning & partitioning.
- Develop model retraining triggers using monitoring signals (data drift, concept drift,
performance decay).
- Implement end-to-end observability for model/API health (Prometheus, Grafana,
ELK, CloudWatch).
- Optimize AWS infra costs with autoscaling, spot instances, and efficient storage.
Requirements:
- Strong expertise in AWS (S3, EKS/ECS, Lambda, SageMaker, Step Functions).
- Hands-on with Databricks, PySpark, advanced SQL, and workflow orchestration (Airflow/Prefect).
- Proven experience in scalable model serving (TF Serving, TorchServe, KServe).
- Deep understanding of monitoring, retraining strategies and infra governance.
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