Engineering Associate

Job Responsibilities: 

• Contribute to multi-cloud platform architecture design and implementation (AWS primary, Azure/GCP secondary). 

• Implement network topologies, IAM roles/policies, and account boundaries. 

• Provision and manage cloud resources using Infrastructure as Code (e.g., Terraform, Ansible) and Git-based workflows. 

• Participate in architecture, design, and change request reviews. 

• Deploy, configure, and operate Kubernetes clusters. 

• Configure and maintain core Kubernetes platform services (ingress, load balancing, storage, autoscaling). 

• Implement and maintain Kubernetes RBAC, namespace structures, and basic network policies. 

• Troubleshoot Kubernetes workloads and cluster-level issues. 

• Build and maintain container images following established standards. 

• Manage and operate container registries and image promotion workflows within CI/CD pipelines. 

• Integrate and operate CNCF ecosystem tools for observability (Prometheus, Grafana, Loki, FluentBit, OpenTelemetry) and deployment/configuration (Helm, GitOps tools). 

• Contribute to the adoption of GitOps workflows for application deployments and platform configuration management. 

• Support deployment and operation of data and streaming platforms on Kubernetes (e.g., Apache Spark, Flink, Kafka). 

• Implement storage and configuration for stateful workloads (persistence, backup, basic performance tuning). 

• Collaborate with data engineering teams to configure resources, optimize job execution, and improve reliability. 

• Operate and troubleshoot Linux-based systems and Kubernetes worker nodes. 

• Implement hardening baselines and security configurations on Linux hosts. 

• Automate OS-level configuration, patching, and maintenance (e.g., Bash, Python, Packer, Ansible). 

• Integrate security checks into CI/CD pipelines (container image, dependency, configuration/policy scanning). 

• Apply policy-as-code tools (e.g., OPA, Kyverno) to enforce platform guardrails. 

• Implement least-privilege IAM roles, encryption standards, and secure secrets management. 

• Participate in incident response and post-incident reviews for platform-related security/reliability events. 

• Implement and manage observability tooling for metrics, logs, and traces across cloud and Kubernetes. 

• Build and maintain dashboards, alerts, and runbooks for common scenarios. 

• Contribute to defining and monitoring Service Level Indicators (SLIs) and SLOs. 

• Assist in capacity planning, performance testing, and cost optimization initiatives. 

• Develop and maintain reusable automation (modules, pipelines, scripts) for infrastructure provisioning and application deployment. 

• Implement CI/CD pipelines (e.g., GitLab CI, Harness) for automated build, test, security scanning, and deployment. 

• Help build self-service capabilities for development and data teams (templates, APIs). 

• Identify manual operational tasks and reduce toil through scripting and automation. 

• Familiarity with AIOps concepts, including ML-based anomaly detection for infrastructure metrics, logs, and security events 

• Experience with predictive analytics for capacity planning, resource utilization, and failure prediction 

• Understanding of automated root cause analysis using AI-driven correlation tools 

• Familiarity with provisioning and managing GPU-enabled workloads for ML training/inference 

• Understanding of ML feature pipelines using Spark, Kafka, and BigQuery 

• Experience with AI-assisted automation for infrastructure provisioning and self-healing systems 

• Familiarity with AI-powered CI/CD optimization, intelligent test selection, and deployment risk assessment 

• Understanding of ML-driven auto-scaling and self-healing mechanisms in Kubernetes environments 

• Collaborate with engineering, data, SRE, and security teams to translate requirements into solutions. 

• Document designs, runbooks, and standards; contribute to internal knowledge bases. 

• Participate in internal training, demos, and brown-bag sessions to promote best practices. 

• Provide guidance and informal mentoring to junior engineers.

Required Qualifications 

• Bachelor’s degree in Computer Science, Engineering, or related technical field, or equivalent practical experience. 

• 4+ years of experience in infrastructure, platform, or cloud engineering roles, with hands-on experience in production environments. 

• Strong practical experience with AWS services such as: 

• VPC networking, security groups, and routing. 

• IAM (roles, policies, federation). 

• EC2, EKS, RDS, S3, SQS, SNS, CloudWatch, and related services. 

• Solid experience running workloads on Kubernetes in production, including: 

• Deploying and managing workloads (Deployments, StatefulSets, DaemonSets, Jobs). 

• Configuring services, ingress, ConfigMaps, and Secrets. 

• Understanding of cluster networking, storage concepts, and basic security controls (RBAC, NetworkPolicies). 

• Strong hands-on experience with containerization: 

• Building secure and efficient container images. 

• Working with container registries and image promotion/tagging strategies. 

• Strong Linux systems skills: 

• System administration, networking basics, shell scripting, and troubleshooting. 

• Automation using Bash and at least one higher-level language (e.g., Python or Go). 

• Practical experience with DevSecOps practices: 

• Implementing CI/CD pipelines (e.g., GitLab CI, Harness Pipelines, or similar). 

• Integrating security and quality checks into build and deployment pipelines. 

• Infrastructure as code using tools such as Terraform, Ansible, or CloudFormation. 

• Hands-on experience with observability tooling: 

• Metrics, logging, and tracing for distributed applications and Kubernetes (e.g., Prometheus, Grafana, Loki, FluentBit, OpenTelemetry). 

• Creation of dashboards, alerts, and basic runbooks. 

• Strong problem-solving and troubleshooting skills in distributed, cloud-native environments. 

• Effective communication and collaboration skills, with the ability to work cross-functionally with engineering, data, and security teams.