Manage model registries, continuous training loops, and A/B testing infrastructure. Deploy agentic models as scalable microservices on Kubernetes and build observability dashboards to track token usage, latency, and agent reasoning paths. Maintain MLOps pipelines and ensure scalable, observable AI infrastructure.
We are seeking a skilled MLOps & Agentic Platform Engineer. This role involves managing model registries, developing continuous training loops, and implementing A/B testing infrastructure. The ideal candidate will have a strong DevOps/MLOps background and be adept at deploying scalable microservices and building observability dashboards.
Responsibilities:
- Manage model registries, continuous training loops, and A/B testing infrastructure.
- Deploy agents as scalable microservices on Kubernetes.
- Build observability dashboards to track token usage, latency, and agent reasoning paths.
Qualifications:
- Strong DevOps/MLOps background (Kubernetes, Docker, Terraform).
- Experience with MLflow, Weights & Biases, or LangSmith.
- Knowledge of building scalable microservice architectures.
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