Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable microservices on Kubernetes; and create observability dashboards tracking token usage, latency, and agent reasoning.
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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