Cloud DevOps Engineer-Junolabs_Hybrid-Bengaluru

Job Description

We are looking for a Cloud DevOps Engineer to build, automate, and scale our product, which is a multi-cloud, event-driven AI system for real-time anomaly detection and alerting. You will be responsible for cloud infrastructure, Kubernetes, automation, CI/CD, and observability, ensuring high availability and performance. While this is a DevOps-heavy role, knowledge of event streaming, real-time data processing, and ML pipeline automation will be a plus. Key Responsibilities Building, automating, and maintaining robust and scalable cloud infrastructure, with a strong emphasis on automation, security, and observability. 1. Infrastructure & Cloud Automation: Multi-Cloud Management: Design, implement, and manage infrastructure across various cloud platforms (e.g., AWS, Azure, GCP) and on-premises environments. IAC Implementation: Utilize Infrastructure-as-Code (IaC) principles to automate infrastructure provisioning and management using Terraform or Pulumi. Configuration Management: Automate system configuration and application deployments using Ansible (or similar configuration management tools) to ensure consistency and reliability. Kubernetes Optimization: Optimize the performance and cost-effectiveness of both cloud-based (EKS, AKS, GKE) or self-hosted Kubernetes clusters. 2. CI/CD & Deployment Automation: Design and implement GitOps workflows using tools like Jenkins, and GitHub Actions to streamline application deployments. Containerized Deployment: Automate the deployment of containerized applications across various Kubernetes distributions (EKS, AKS, GKE). API Management: Manage API gateways, service discovery, and ingress controllers using technologies like Nginx. 3. Event-Driven & Real-Time Processing (Supporting Role): Real-Time Data Streaming: Deploy, manage, and maintain Kafka-based solutions (Apache Kafka, Confluent, MSK, Event Hubs, Pub/Sub) for real-time data ingestion and processing. Database Optimization: Optimize the performance and scalability of various open-source and cloud-based databases (PostgreSQL, TimescaleDB, Elasticsearch, Influx DB etc). MLOps Support: Support Machine Learning (ML) workloads by integrating Kubeflow, MLflow, Ray, Seldon, Sage maker, Vertex AI, and Azure ML with CI/CD pipelines. 4. Observability & Security: Monitoring & Logging: Implement comprehensive monitoring and logging solutions using tools like Prometheus, Grafana, Open Telemetry, ELK, Loki, and Datadog to proactively identify and resolve issues. Security Implementation: Implement robust security measures, including RBAC, IAM, and Secrets Management (Vault, Sealed Secrets, AWS KMS, Azure Key Vault) to protect sensitive data and infrastructure. Compliance & Best Practices: Ensure adherence to SOC2, GDPR, and other relevant compliance standards, while implementing security best practices across all cloud and open-source technologies. Skills & Qualifications: Must-Have: Strong expertise in Cloud DevOps (AWS, Azure, GCP) and open-source infrastructure. Experience with Terraform, Kubernetes (EKS, AKS, GKE, OpenShift, K3s), Docker and CI/CD automation. Hands-on experience with Kafka, RabbitMQ, or event-driven microservices. Observability tools (Prometheus, Open Telemetry, Grafana, ELK). Strong Python or scripting for automation. Good-to-Have (Data & AI Integration): Knowledge of real-time data pipelines (Kafka, CDC, Flink, Spark Streaming, Debezium). Experience integrating MLOps tools (Kubeflow, MLflow, DASK, Ray, Sage maker, Vertex AI, Azure ML). Exposure to serverless computing (Lambda, Cloud Functions, Azure Functions).