Senior MLOps Engineer – LLMOps

Write high-quality, maintainable software — primarily in Python Strong background in scalable infrastructure, including containerization and orchestration (e.g. Docker, Kubernetes) Experience with Infrastructure-as-code and deployment (e.g. Terraform, CI/CD pipelines) Experience with Monitoring and logging frameworks (e.g. Datadog, Prometheus, OpenTelemetry) Understand and implement ML Ops best practices, including model versioning and rollback strategies Experience with Automated evaluation and drift detection Experience with Scalable model and agent serving infrastructure (e.g. vLLM, Triton, BentoML) Deploy and maintain LLM and agentic workflows in production Experience with Monitoring cost, latency, and performance Experience with Capturing traces for analysis and debugging Experience with Optimizing prompt/response flows with real-time data access Strong ownership and pragmatism

Build reusable CI/CD workflows for model training, evaluation, and deployment Automate model versioning, approval workflows, and compliance checks Build out a modular and scalable AI infrastructure stack Partner with engineering and data science to embed AI models and agents Continuously evaluate and integrate state-of-the-art AI tools Drive AI reliability and governance Build and enhance AI/ML Model Performance Ensure data accuracy, consistency and reliability Deploy infrastructure to support offline and online evaluation of LLMs and agents Enable researchers to iterate quickly by providing sandboxes, dashboards, and reproducible environments