Devops Engineer

Airtel; Gurugram District; On-site; INR 800,000 - 1,400,000; Full time

Job summary

Airtel is seeking an experienced AI Platform / Kubernetes Application Engineer to lead development, containerization, deployment, and operation of cloud-native applications on Kubernetes/OpenShift. The role emphasizes hands-on work with Docker/Podman, Python-based APIs, and scalable CI/CD and GitOps practices.

Ideal candidates will have strong Linux scripting, REST APIs, and experience with observability stacks, databases, and GPU-enabled workloads in enterprise environments.

Qualifications

• Experience delivering Kubernetes-based applications and microservices in production.

Responsibilities

• Develop, containerize, deploy, and operate applications on Kubernetes/OpenShift.
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• CoT_job_summary_short
• COMPANY NAME: Airtel
• KEY POINTS: Kubernetes, CI/CD, GitOps, cloud-native engineering

Skills

Kubernetes-based app development

Platform engineering

DevOps

Cloud-native software engineering

OpenShift

Python

FastAPI/Flask/Django

Docker/Podman

Container security practices

CI/CD tools

GitOps

Observability tools

Linux/bash scripting

REST APIs/YAML

Databases (PostgreSQL, MongoDB, Redis)

GPU-enabled workloads (NVIDIA)

Tools

Docker/GitOps tools

Kubernetes tools (Helm, Kustomize)

CI/CD platforms (GitLab CI, Jenkins, Argo CD, Tekton, GitHub Actions)

Observability stacks (Prometheus, Grafana, Loki, Elasticsearch, OpenSearch)

Job description

Key Responsibilities:

AI Platform / Kubernetes Application Engineer

• 24 years of professional experience in Kubernetes-based application development, platform engineering, DevOps, or cloud-native software engineering.
• Strong hands-on experience developing, containerizing, deploying, and operating applications on Kubernetes or OpenShift.
• Proficiency in Python and experience building production-grade APIs and microservices using frameworks such as FastAPI, Flask, or Django.
• Strong experience with Docker or Podman, including container image creation, multi-stage builds, image optimization, registry management, and container security practices.
• Experience creating and maintaining Kubernetes resources such as Deployments, StatefulSets, Services, Ingress, ConfigMaps, Secrets, Jobs, CronJobs, Persistent Volumes, and Network Policies.
• Hands-on experience with Helm, Kustomize, Kubernetes Operators, or similar deployment and configuration-management tools.
• Strong understanding of Kubernetes concepts, including pod lifecycle, scheduling, probes, resource requests and limits, autoscaling, storage, networking, RBAC, and service discovery.
• Experience troubleshooting application, container, networking, storage, and resource-related issues in Kubernetes environments.
• Practical experience with CI/CD tools such as GitLab CI, Jenkins, Argo CD, Tekton, or GitHub Actions.
• Experience implementing GitOps-based deployment and application lifecycle-management practices.
• Exposure to observability tools such as Prometheus, Grafana, Loki, Elasticsearch, OpenSearch, or distributed tracing platforms.
• Strong understanding of Linux, shell scripting, REST APIs, YAML, Git, networking fundamentals, and secure application configuration.
• Experience working with databases and data platforms such as PostgreSQL, MongoDB, Elasticsearch, ClickHouse, Redis, or vector databases.
• Experience deploying stateful and stateless applications in enterprise or air-gapped environments is highly desirable.
• Familiarity with secrets management, image vulnerability scanning, role-based access control, TLS certificates, and enterprise security controls.
• Experience with GPU-enabled Kubernetes workloads, NVIDIA GPU Operator, model-serving platforms, or GPU resource management is an advantage.

Secondary AI/ML Skills

• Basic to intermediate understanding of machine learning, deep learning, and generative AI concepts.
• Exposure to Python ML libraries such as Pandas, NumPy, scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Familiarity with LLMs, transformers, Hugging Face, Ollama, RAG frameworks, MCP, LangChain, Haystack, CrewAI, or custom agent orchestration is preferred but not mandatory.
• Exposure to model serving, inference APIs, model quantization, MLflow, Weights & Biases, or other model and experiment-tracking tools is an added advantage.
• Familiarity with multimodal models and AI data pipelines is beneficial.