Tech Stack

Description

You will own and scale systems powering real-world customer interactions, driving high-impact initiatives like GPU orchestration, self-hosting, and low-latency AI deployments while working closely with ML teams to productionize models. You have end-to-end ownership, a modern tech stack, and the opportunity to shape MLOps best practices.

Requirements

• 6+ years in DevOps, SRE, or Cloud Infrastructure roles, preferably in AI or data-intensive environments.
• Strong expertise in Kubernetes (EKS, AKS) for deploying AI workloads and managing GPU clusters.
• Experience with self-hosting Elasticsearch, Prometheus, Grafana, Kafka.
• Hands-on with Terraform, CloudFormation, and cloud platforms (AWS, Azure, GCP).
• Strong automation skills in Python, Bash, or Go.

Responsibilities

• Lead transition from managed services to self-hosted Elasticsearch, Prometheus, etc.
• Optimize AI infrastructure for deploying and scaling ML models with low latency.
• Design scalable, fault-tolerant systems for large-scale AI workloads and data pipelines.
• Enhance and automate ML model deployment pipelines using Kubeflow, MLflow, Argo Workflows.
• Implement monitoring, logging, and alerting strategies with Prometheus, Grafana, ELK, OpenTelemetry.