MLOps Engineer, ML Pipelines & CI/CD
Overview
You will own the design, deployment, and management of scalable machine learning pipelines in production, serving models that power enterprise systems. Your work integrates ML models with AWS, Kubernetes, and Terraform, ensuring reliability and automation. You'll collaborate with data scientists and software engineers to streamline model delivery, and drive governance and monitoring for model performance. This role stands out with its focus on owning the full MLOps lifecycle from data ingestion to model retraining.
What You'll Do9
- 1Design and implement end-to-end ML pipelines from data ingestion to model deployment, using AWS and Kubernetes.
- 2Build and manage CI/CD pipelines for ML models, covering training, testing, and deployment, with Jenkins and GitHub Actions.
- 3Automate model retraining and deployment workflows with Terraform and Docker to reduce deployment time by 50%.
- 4Monitor model performance and data drift, setting up alerting and logging with Prometheus and Grafana.
- 5Lead the integration of ML models into existing production systems, ensuring low-latency serving with SageMaker and KServe.
- 6Drive the adoption of version control for data, code, and models, using DVC and MLflow.
- 7Scale inference and training workloads on Kubernetes, optimizing resource usage with Karpenter and Spot Instances.
- 8Collaborate with data scientists to package and deploy models, ensuring reproducibility and compliance with MLflow.
- 9Contribute to the governance framework for ML models, including security and access control using IAM and VPC.
Requirements9
- 13+ years of experience in MLOps or DevOps with a focus on ML pipelines.
- 2Strong proficiency in Python, Docker, and Kubernetes.
- 3Experience with CI/CD tools like Jenkins or GitHub Actions.
- 4Hands-on experience with cloud platforms, preferably AWS (SageMaker, Lambda, S3).
- 5Familiarity with MLflow, Kubeflow, or similar ML orchestration tools.
- 6Knowledge of model monitoring and logging with Prometheus and Grafana.
- 7Experience with infrastructure-as-code using Terraform or CloudFormation.
- 8Understanding of data pipelines and ETL processes with Spark or Airflow.
- 9Bachelor's degree in Computer Science, Engineering, or related field.
Salary Insight
$125 - $131k per year
Location
Required Skills
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