LLMOps Architect
Tasks
- Build CI/CD pipelines for ML including versioning and deployment
- Coach researchers transitioning into engineering roles
- Deploy and manage real-time and batch model inference systems
- Design ML platform for training, serving, and retraining models
- Evaluate and build tools for model registry, feature stores, and orchestration
- Implement governance, security, IAM, and model explainability
- Lead incident response and root cause analysis for ML uptime
- Optimize cloud usage and costs for ML workflows
- Partner with teams to productionize GenAI and AI models
- Promote best practices in MLOps and mentor teams
- Set up observability and alerts for ML systems
Perks/Benefits
- Collaboration with experts
- Competitive salary
- Equity
- Fast-paced environment
- Generous leave
- Growth opportunities
- Healthcare
- Ownership
- Team-focused culture
Skills/Tech-stack
AI Coding Agents | AI Safety | AI coding | AWS | Anthropic | ArgoCD | Automation | CI/CD | Cloud Security | Coding Agents | Cost Optimization | Data Drift | Data Drift Detection | Docker | Drift Detection | Experimentation | GitHub Actions | IAM | Jenkins | Kubernetes | Langsmith | ML Infrastructure | MLOps | MLflow | Model Explainability | Model Monitoring | Model Serving | OpenAI | Python | RAG | Reinforcement Learning | Responsible AI | Terraform | Vector DBs
Education
Bachelor's | Master's | PhD
Roles
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