#EG Senior / LLMOps Engineer
Tasks
- Build CI/CD pipelines for AI services
- Configure alerting and runbooks for production incidents
- Contribute reusable deployment patterns to internal asset library
- Define and track SLAs and SLOs for AI services
- Deploy RAG and agentic applications
- Implement safe rollout and rollback
- Lead incident response for critical issues
- Manage production operations patching upgrades and incident response
- Mentor engineers on LLMOps practices
- Monitor token spend and inference cost
- Optimize model routing for cost performance
- Own LLM deployment pipeline
- Set up observability for LLM latency errors and drift
- Version models prompts configurations as code
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Argo CD | Azure | Azure OpenAI | Bedrock | CI/CD | Docker | ELK | GitHub Actions | GitLab | Go | Google Cloud | Grafana | Helm | Infrastructure as Code | JavaScript | Jenkins | Kubernetes | Langgraph | Litellm | Model gateway | OpenSearch | OpenTelemetry | PGVector | Pinecone | PostgreSQL | Prometheus | Prompt engineering | Prompt versioning | Python | RAG | Redis | Semantic Kernel | TGI | Terraform | TypeScript | VLLM | Vault | Vertex AI | Weaviate | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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