DevOps Engineer

Job Description

About Forage AI Forage AI builds next-generation systems for large-scale data collection, processing, and automation. Our platforms handle web crawling, document parsing, distributed data pipelines, and AI-driven data systems. Our stack is primarily Python and cloud-native infrastructure on AWS, with exposure to GCP and Azure. We emphasize reliable infrastructure, scalable architecture, and strong engineering practices. Increasingly, we are integrating GenAI and AI agents into our systems. We operate in a high-ownership environment, where engineers are responsible not only for building systems but also ensuring they are reliable, scalable, and production ready. Location: Remote (Work from Home) Role Overview We are looking for a DevOps Engineer with strong software engineering capabilities. This role focuses on designing and operating cloud infrastructure, CI/CD systems, deployment automation, and observability platforms, while also writing production-grade automation and platform tooling in Python. You will work closely with developers to build reliable, scalable, and secure infrastructure that powers large-scale data processing systems. This role requires someone who can think like both an infrastructure engineer and a software engineer. Salary up to Rs. 9 LPA depending on your technical interview Key Responsibilities: Cloud Infrastructure & System Design Design and manage scalable cloud infrastructure primarily on AWS Architect systems using services such as EC2, ECS/EKS, Lambda, S3, RDS, DynamoDB, SQS/SNS, CloudWatch Design cloud-native architectures that are resilient, scalable, and cost efficient. Build systems that remain cloud agnostic where possible. CI/CD & Deployment Automation Build and maintain CI/CD pipelines for automated testing, build, and deployment. Improve deployment reliability and speed through automation and release strategies Implement blue-green, rolling, and canary deployments. Infrastructure as Code Manage infrastructure using Terraform, CloudFormation, or similar tools Automate infrastructure provisioning and environment setup. Platform Engineering & Developer Productivity Build internal tools and automation scripts in Python to improve developer workflows. Create standardized deployment frameworks for applications and services Support development teams with infrastructure design and operational best practices. Observability & Reliability Implement logging, monitoring, alerting, and tracing systems. Maintain system reliability through proactive monitoring and incident response. Define and monitor SLOs, SLAs, and system health metrics. Security & Best Practices Implement security best practices including IAM policies, secrets management, and least-privilege access. Ensure infrastructure and deployments follow secure and compliant patterns. Collaboration Work closely with software engineers, QA teams, and data engineers. Participate in architecture discussions and infrastructure design reviews. Required Qualifications 3-5 years of experience in DevOps, infrastructure engineering, or backend platform engineering. Strong experience with AWS cloud infrastructure. Hands-on experience with CI/CD systems (GitHub Actions, Jenkins, GitLab CI, etc.). Experience with containerization technologies such as Docker. Exposure to container orchestration platforms such as Kubernetes or ECS. Strong scripting/programming skills in Python & amp; SQL. Experience with Linux systems and networking fundamentals. Understanding of distributed systems and microservices architectures. Strong experience with Git and modern development workflows. Preferred / Good to Have (Prioritized) Infrastructure & amp; DevOps Infrastructure as Code using Terraform or Cloud Formation Experience running Kubernetes clusters in production. Experience with AWS cost optimization and scaling strategies Data Platforms Exposure to data pipelines and distributed data systemsExperience with Airflow, Kafka, Spark, or large-scale ETL Workflows. Observability & Reliability Experience with Prometheus, Grafana, ELK stack , or Datadog Multi-Cloud Experience with GCP or Azure GenAI / AI Infrastructure Experience building infrastructure for LLM pipelines, vector databases, or AI systems How We Work: Engineers own systems end-to-end: design → build → deploy → operate Strong emphasis on automation, reliability, and observability. Incremental delivery through small pull requests and clear design discussions Collaborative culture with high ownership and accountability. Work‑from‑Home Requirements High‑speed internet for calls and collaboration. A capable, reliable computer (modern CPU, 16GB+ RAM). Headphones with clear audio quality. Stable power and back up arrangements. Forage AI is an equal opportunity employer. We value curiosity, craftsmanship, and collaboration.