MLOps Engineer
Tasks
- Build autoscaling and capacity management
- Build inference platforms for large machine learning models
- Collaborate with ML and product teams for model releases
- Design model serving platforms for production
- Develop deployment workflows canary shadow testing and rollback
- Document operational procedures and performance tuning
- Drive end to end observability and error tracking
- Implement caching prompt deduplication and response reuse
- Implement multi tenant routing rate limiting and quality of service
- Implement security controls request signing content filtering and abuse detection
- Integrate model serving with API gateways identity and observability
- Operate incident response for high availability AI services
- Optimize inference performance
- Tune GPU utilization memory management and KV cache
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | Abuse detection | Automated rollback | Autoscaling | C++ | Caching | Canary Releases | Capacity Planning | Content Filtering | Distributed Systems | GPU Architecture | GPU Utilization | Go | KV cache | Kubernetes | Language Models | Large Language Models | Machine Learning | Memory Management | Metrics | Model Serving | Observability | Performance Engineering | Python | Quality of Service | Rate Limiting | Request Signing | Rust | Shadow testing | Structured Logging | TensorRT-LLM | Tracing | VLLM
Education
Roles
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