Software Systems Engineer-devops

Required Qualifications:

• B.Tech or M.Tech in Computer Science or related major.
• 0–2 years of hands-on experience (including internships, co-ops, or academic projects) writing high-quality code in languages such as Python, Golang, Java, C, or C++.
• Foundational understanding of Distributed systems, Cloud Native software development, Microservices, and Infrastructure as Code concepts.
• Exposure to Virtual Infrastructure Management software like KVM, OpenStack, Kubernetes, or VMware through coursework, projects, or internships.
• Familiarity with one or more infrastructure components like Cassandra, RabbitMQ, Elastic Search, Docker, Prometheus, or Grafana (hands-on project experience is a plus).
• Interest in writing software with Testability, Observability, and Debuggability in mind for Cloud and On-prem solutions.
• Willingness to learn the challenges of operating large-scale software and hardware systems and contribute to solutions.
• Exposure to deployment and automation tooling such as Ansible, Helm, or Terraform (academic or personal projects acceptable).
• Ability to write code, develop automated test cases, and learn to integrate them into CI/CD pipelines for continuous execution and quality management.
• Understanding of building software leveraging REST or gRPC based APIs.
• Customer-first mindset with interest in building and automating frameworks for UI/UX using modern automation and monitoring tools.
• Attention to detail and willingness to learn monitoring infrastructure concepts such as OpenTelemetry and tools like Datadog or Signoz.
• Actively participate in design discussions, ask questions, and learn from architectural decision-making with cross-functional stakeholders.
• Collaborate well with peers and senior engineers, be receptive to feedback and mentorship, and ramp up quickly on team technologies.
• Curious to learn and stay on top of the latest technology and tooling advancements, stay humble, and contribute to solving customer problems.

Preferred Qualifications:

• Familiarity with AI/ML and AI Agentic frameworks and tools such as LangChain, FastAPI (coursework or projects count).
• Exposure to AIOps/MLOps concepts or tools such as SageMaker, Databricks, Vertex AI, Azure ML, MLFlow.
• Exposure to Cloud Native GitOps tools (ArgoCD, Argo Workflow, etc.).
• Academic publications, hackathon participation, open-source contributions, or involvement in technical communities or professional organizations such as IEEE.