MLOps / AI Infrastructure Engineer
Tasks
- Automate ML infrastructure and observability
- Build and maintain model deployment pipelines
- Build and maintain model serving architectures for LLMs
- Design and maintain ML infrastructure
- Develop CI CD pipelines for ML lifecycle
- Manage Docker container workloads
- Manage Kubernetes orchestration
- Monitor GPU and CPU utilization
- Optimize cloud compute and cost
- Troubleshoot ML infrastructure performance issues
Perks/Benefits
- Collaborative environment
- Exposure to modern AI ML infrastructure
- Full-time employment
- Remote work from India
- Technical ownership
Skills/Tech-stack
AWS Bedrock | Amazon SageMaker | BentoML | CI/CD | Docker | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Infrastructure as Code | Kubernetes | Machine Learning | Milvus | Model Deployment | Model Serving | Monitoring | Observability | Pinecone | Python | Qdrant | Ray | Terraform | Triton | Vector Databases | Vertex AI | “as-code”
Education
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