Exacaster

DevOps Engineer

• Design, deploy, and maintain cloud and on-premises infrastructure for big data platforms and Generative AI workloads
• Build secure, scalable AWS or Azure environments, including multi-account/landing-zone setups, networking, IAM, and identity integration
• Deploy, scale, and operate production LLM-powered services, including agent frameworks, RAG pipelines, vector stores, and orchestration layers
• Ensure AI services are reliable, observable, and cost-efficient
• Use Terraform and Ansible for repeatable deployments
• Manage secrets, keys, and AI provider credentials securely using AWS Secrets Manager, Vault, and KMS
• Build and maintain CI/CD pipelines with GitLab CI and ArgoCD for infrastructure, data applications, and AI services
• Manage automated testing, static analysis, AI-assisted code review, and release processes
• Deploy and operate EKS/AKS Kubernetes clusters for big data and AI workloads
• Optimize GPU/CPU resource utilization for inference and batch jobs
• Implement and optimize observability using Prometheus, Zabbix, Grafana, and cloud-native tools
• Monitor LLM-specific signals including model latency, error rates, prompt/response quality, and cost
• Collaborate with network and security teams on secure connectivity, VPNs, Transit Gateway, and private endpoints
• Apply compliance best practices in regulated industries
• Identify and resolve performance bottlenecks across big data clusters and AI inference workloads
• Use AI tools such as Claude Code and MCP-based assistants for code review, IaC generation, runbook automation, incident response, and team adoption

Senior DevOps Engineer – Big Data, AI Platforms