Senior DevOps/ MLOps Engineer
Tasks
- Architect MLOps pipelines
- Automate deployment using containers
- Automate infrastructure with scripting
- Benchmark and profile inference workloads
- Build Kubernetes clusters on AWS EKS
- Create infrastructure as code
- Deploy and maintain model serving infrastructure
- Deploy canary and blue green releases
- Develop CI/CD pipelines for MLOps
- Implement auto scaling for model workloads
- Manage Kubernetes networking and service mesh
- Monitor GPU and model telemetry
- Optimize GPU inference performance
- Set up observability with metrics logs traces
Perks/Benefits
- Annual health checkups
- Collaborative learning opportunities
- Health insurance
- Preferential employee pricing
Skills/Tech-stack
AWS | ArgoCD | Bash | CI/CD | CUDA | CloudFormation | Docker | Dynamic batching | EKS | ELK Stack | GPU Performance | GPU performance tuning | GitHub Actions | GitLab CI | Grafana | Helm | Inference Server | Infrastructure as Code | Kubernetes | Language Models | Large Language Models | Model Serving | NVIDIA MIG | Performance Tuning | Prometheus | Python | Quantization | Tensor Parallelism | Terraform | Triton Inference | Triton Inference Server | VLLM | “as-code”
Education
Roles
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