AI Production Engineer
Tasks
- Build LLM RAG agents and inference pipelines
- Build automation self healing systems
- Build observability with alerting and monitoring
- Create CI/CD pipelines
- Deploy and operate on AWS Azure GCP
- Design production grade AI ML systems
- Develop mlops infrastructure
- Implement reliability by design resilience and circuit breakers
- Lead design reviews mentor engineers
- Manage AI infrastructure training inference data pipelines
- Manage GPU fleet and model serving
- Participate in on call escalation for incidents
- Travel to engage executive partners
- Write and review production code
Perks/Benefits
Skills/Tech-stack
AWS | Automation | Azure | C++ | CDN | CI/CD | Capacity Planning | Circuit Breakers | Cloud platform | Datadog | Distributed Systems | GPU infrastructure | Go | Google Cloud | Google Cloud Platform | Grafana | Inference Optimization | Java | Kubernetes | LLM | Linux | Load Balancing | MLOps | Memcached | Model Serving | MySQL | Networking | Observability | Prometheus | Prompt engineering | Python | RAG | Redis | Rust | Self-healing | Self-healing systems | Terraform | Unix
Education
Roles
AI | AI Production Engineer | Engineer | Production Engineer | Software Engineer
Regions
Countries
States
Cities
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