Container Engineer

4 days ago

Singapore ELLIOTT MOSS CONSULTING PTE. LTD. Full time

Job Summary We are seeking a highly skilled Kubernetes / Container Engineer to design, build, and optimize our containerized infrastructure for AI-driven microservices. The ideal candidate will have deep experience with Amazon EKS, service mesh, and microservice deployment patterns, ensuring scalable, secure, and high-performing workloads across multiple environments. Key Responsibilities EKS Cluster Management Set up, configure, and maintain Amazon EKS clusters across development, staging, and production environments. Implement robust monitoring, scaling, and upgrade strategies for cluster health and performance. AI Microservices Deployment Deploy and manage containerized AI microservices (e.g., embeddings, rerankers, guardrails). Optimize GPU-enabled workloads within Kubernetes for model serving and inference. Container Orchestration & Scaling Define and implement autoscaling policies (HPA, VPA, cluster autoscaler). Manage container scheduling, resource allocation, and performance tuning. Service Mesh & Networking Configure and operate service mesh solutions (e.g., Istio, Linkerd) for observability, security, and traffic management. Manage network policies, ingress/egress controllers, and secure communication between services. Helm & Configuration Management Develop and maintain Helm charts for application deployment and configuration standardization. Automate deployment pipelines with GitOps / CI-CD practices. Container Security & Compliance Enforce image scanning, runtime security, and least-privilege access across clusters. Implement best practices for secrets management and container isolation. Architecture & Optimization Collaborate with developers and AI engineers to design microservices architectures optimized for reliability and cost. Contribute to system design decisions, performance benchmarking, and capacity planning. Required Skills & Experience Deep hands-on experience with Kubernetes and Amazon EKS (multi-cluster and multi-environment setups). Strong understanding of container orchestration, Helm, and service mesh (e.g., Istio, Linkerd). Proven experience deploying AI or ML microservices (especially GPU workloads). Knowledge of Kubernetes networking, security best practices, and autoscaling mechanisms. Proficiency in Docker, YAML, Terraform, and CI/CD tools (e.g., ArgoCD, Jenkins, GitHub Actions). Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry). Strong grasp of microservices architecture patterns and DevOps methodologies. Experience with KServe, Kubeflow, or other AI model deployment frameworks. Familiarity with AWS infrastructure (EC2, IAM, VPC, ALB/NLB, CloudWatch). Scripting experience in Python, Bash, or Go for automation. Education Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience #J-18808-Ljbffr

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