MLOps Engineer
Tasks
- Build inference platforms for production
- Collaborate with ML and product teams for model releases
- Create deployment workflows with canary and rollback
- Design model serving platforms
- Develop autoscaling and capacity management
- Document operational procedures and performance tuning
- Drive end-to-end observability
- Implement caching and prompt deduplication
- Implement multi tenant routing rate limiting and quality of service
- Implement security controls at the serving layer
- Integrate model serving with API gateways and observability
- Operate incident response for high availability services
- Optimize inference performance
- Tune GPU utilization and memory management
Perks/Benefits
Skills/Tech-stack
API Gateway | Access Management | Autoscaling | C++ | Caching | Canary Releases | Distributed Systems | GPU Architecture | Go | Identity and Access Management | Identity and access | Incident Response | Kubernetes | Large Language Model | Large language model inference | Machine Learning | Metrics | Model Inference | Observability | Python | Rate Limiting | Request Signing | Rust | Security Engineering | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
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