MLOps for LLM Systems at Scale
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We help remote-first startups hire MLOps engineers who specialize in deploying, monitoring, and scaling LLM-powered systems. These engineers ensure your GenAI stack runs reliably in production — not just in notebooks.
From experimentation to stable infrastructure.
LLM Deployment & Serving
Containerizing models, managing inference pipelines, and optimizing GPU or cloud environments for stable serving.
Monitoring & Observability
Tracking latency, token usage, hallucination patterns, failure rates, and system health in live production.
CI/CD for AI Systems
Automating model updates, version control, rollback strategies, and continuous evaluation pipelines.
How We Assess MLOps Engineers
Screening focuses on operational depth and real-world implementation:
- Experience with model serving frameworks
- Infrastructure setup on AWS, GCP, or Azure
- Experiment tracking and model versioning
- Logging, monitoring, and alert systems
- Cost optimization and scaling strategies
Why This Role Matters
LLM systems introduce new operational challenges like unpredictable latency, evolving models, and heavy compute requirements. Strong MLOps engineers bridge research and production by creating infrastructure that is secure, observable, and scalable.
We prioritize engineers who have operated AI systems in live environments not just supported traditional ML workflows.