ML Infrastructure Engineer
Tasks
- Automate end to end ML lifecycle workflows
- Build production ML deployment and inference systems
- Collaborate with data scientists and engineers on end to end ML workflows
- Deploy and manage ML workloads on AWS and GCP
- Design scalable solutions for high velocity ML development
- Develop infrastructure as code for scalable repeatable secure environments
- Establish ML model lifecycle management best practices
- Evaluate and implement model serving frameworks
- Implement CI CD workflows for ML automation
- Implement monitoring for model performance latency drift and infrastructure health
- Own ML infrastructure roadmap
- Support governance reproducibility and auditability
- Translate experimentation needs into production ready infrastructure
Perks/Benefits
Skills/Tech-stack
AWS | Amazon Elastic Kubernetes Service | Amazon SageMaker | BigQuery | CD pipelines | CI/CD | CI/CD pipelines | CloudFormation | CloudWatch | Distributed Training | Docker | Drift Detection | Elastic Kubernetes Service | Feast | Feature Store | Flask | GCP | GPU Computing | Google Kubernetes | Google Kubernetes Engine | Grafana | Infrastructure as Code | Kubernetes | Kubernetes Engine | Kubernetes Service | Learning operations | MLflow | Machine Learning | Machine Learning Operations | Model Drift | Model Drift Detection | Model Monitoring | Observability | Prometheus | Python | Terraform | Weights and Biases | “as-code”
Education
N/A
Regions
Countries
States
Cities
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