Location : Gurugram WFO

ABOUT AWIROS :

Awiros is a Series Afunded deep-tech organization (~$7M raised) building the worlds first app-based Operating System for Computer Vision. Founded in 2015, we enable developers and enterprises to build, deploy, and scale cutting-edge AI-powered applications with speed and efficiency. Our platform is built for real-time video/image inference, distributed processing, and high-throughput data handling powered by tools like TensorFlow, Kubernetes, Kafka, and ElasticSearch. Behind it is a stellar team of researchers and engineers, focused on bringing the latest in Deep Learning and AI research to real-world applications. At Awiros, we are not just shaping the future of Computer Vision we're making it accessible to everyone.

About the role :

As a DevOps Engineer at Awiros, you will be part of the core platform engineering effort that enables large-scale AI workloads to run reliably across cloud, on-premise, and hybrid environments. This is a build-oriented role, where you will design, automate, and evolve deployment systems rather than merely operating them. You will work closely with application, platform, and AI teams to develop scalable CI/CD pipelines, cloud-native infrastructure, and deployment frameworks that support real-time, GPU-driven workloads. This role is ideal for engineers who enjoy building systems, experimenting with modern DevOps tooling, and owning infrastructure as a product.

What youll do :

- Design, build, and continuously improve CI/CD pipelines to accelerate development and production releases

- Develop and maintain containerized applications using Docker and Kubernetes

- Build and manage large-scale distributed deployments across cloud, on-premise, multi-site, and air-gapped environments

- Work with GPU-enabled infrastructure and master-worker architectures to support AI inference workloads

- Design and maintain highly available Kubernetes clusters, database clusters, and message queues

- Develop and maintain Helm charts for multiple microservices and platform components

- Build and version deployment stacks, including Kafka, queue systems, and supporting middleware

- Implement Infrastructure as Code using tools such as Terraform, CloudFormation, or Ansible

- Collaborate with development teams to design new deployment workflows and automation frameworks

- Improve observability by building and maintaining logging, monitoring, and alerting systems

- Integrate DevSecOps practices into CI/CD pipelines using tools such as SonarQube, Trivy, or similar

- Own and evolve internal DevOps tooling to support fast, reliable, and secure releases

What were looking for :

- 1+ years of hands-on experience in DevOps, Platform Engineering, SRE, or Cloud Engineering

- Strong practical experience with Docker and Kubernetes

- Experience building CI/CD pipelines using Jenkins, Git-based workflows, and GitOps practices

- Hands-on exposure to cloud platforms such as AWS or GCP

- Experience working with distributed systems such as Kafka, RabbitMQ, Redis, or similar technologies

- Familiarity with Infrastructure as Code tools like Terraform and CloudFormation

- Scripting skills in Bash, Python, or equivalent languages

- Exposure to DevSecOps tooling and secure pipeline practices

- Strong debugging, analytical thinking, documentation, and collaboration skills

- Experience with monitoring and logging stacks such as Prometheus, Grafana, ELK/EFK

Good to have :

- Exposure to additional cloud platforms such as Azure or Oracle Cloud

- Understanding of cloud-native, microservices-based system design

- Experience managing multi-environment deployments (dev, staging, production)

- Knowledge of security best practices in containerized and cloud environments

- Relevant certifications (AWS, Kubernetes, or DevOps-focused)

WHY JOIN AWIROS?

- Build and scale real-time AI and Computer Vision platforms used in production environments

- Work on complex, distributed systems spanning cloud, edge, and on-premise deployments

- Learn by building in a high-ownership, deep-tech engineering culture

- Collaborate closely with experienced platform, AI, and systems engineers

- Influence how modern DevOps and platform engineering practices are implemented at scale