Senior Platform Engineer

Job Description

We are seeking a Senior Platform Engineer with expertise in supporting Kubernetes and Kafka across on-premises and multi-cloud environments (Azure, GCP). The ideal candidate will also be eager to learn about and assist with integrating edge computing platforms, enabling AI technology stacks, and utilizing a robust DevOps toolset. This role requires hands-on Kubernetes experience and a strong foundation in modern platform solutions. Kubernetes Management : Provision, manage, and maintain Kubernetes clusters in on-premises and cloud environments (Azure, GCP). Serverless & Service Mesh : Deploy and manage Knative for serverless workloads and Istio for service mesh implementations. Custom Kubernetes Development : Create custom Kubernetes operators and controllers using Go to extend Kubernetes functionality. Edge & AI Integrations : Integrate edge computing platforms with AI/ML workloads (e.g., TensorFlow Serving, PyTorch, Kubeflow) for efficient edge processing. Collaboration : Work closely with AI/ML, data engineering, and platform teams to optimize resource utilization and scalability for edge and AI workloads. Kafka Administration : Build and administer Kafka clusters to support Kafka Connect in containerized environments. Handle partitioning strategies and optimize Kafka performance. Automation & Provisioning : Develop Ansible playbooks for infrastructure automation and platform provisioning. CI/CD Pipelines : Design and implement GitOps workflows using Jenkins and GitHub/GitLab, ensuring continuous integration and deployment. Monitoring & Logging : Implement robust monitoring with Prometheus and Grafana, and manage centralized logging via ELK or Fluentd. Optimization & Troubleshooting : Apply best practices for performance, platform resilience, and disaster recovery. Diagnose complex issues related to Kafka pipelines, Kubernetes orchestration, and CI/CD processes. Core Skills Kubernetes (5+ years) : Cluster management, deployments, Helm charts, operators, and scaling/integrations. Service Mesh : Working knowledge of Istio or Linkerd for secure microservices communication. Edge Computing : Experience with platforms such as Azure IoT Edge, AWS Greengrass, or GCP Edge TPU is a plus. AI/ML Infrastructure : Experience deploying AI/ML workloads (e.g., TensorFlow Serving, PyTorch, Kubeflow). Cloud Platforms : Hands-on experience with at least two of Azure or GCP. Terraform & Ansible : Proficient in infrastructure provisioning and configuration management. CI/CD : Familiarity with Jenkins, GitHub Actions, GitLab, or similar tools. Go (Golang) / Python : Ability to develop Kubernetes-native solutions (operators, CRDs, controllers). Kafka : Knowledge of Kafka cluster setup, topic management, partitioning, broker optimization, and Kafka Connect is beneficial. Monitoring & Logging : Expertise with Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), or Fluentd.