Key Responsibilities:

- Design and implement cloud architecture for AI/ML, LLM model deployment, monitoring, and scaling
- Deploy models using Docker, Kubernetes, and CI/CD pipelines across cloud environments on Azure or GCP
- Optimize AI model serving using tools like TensorFlow Serving, TorchServe, ONNX Runtime, Triton Inference Server, etc.
- Build and manage IaC using Terraform, or CloudFormation
- Implement security best practices, autoscaling, logging, monitoring (Prometheus/ Grafana/ ELK), and disaster recovery plans
- Collaborate with ML engineers to produce prototypes into resilient, cloud-native services
- Benchmark and tune deployments for low latency, high throughput, and cost optimization

Required Qualifications:

- 8+ years of experience in cloud engineering, with 4+ years focused on AI/ML deployment at scale
- Robust hands-on expertise in Azure and GCP
- Proficient in Docker, Kubernetes, Helm, and serverless deployment models
- Solid understanding of ML frameworks (TensorFlow, PyTorch, scikit-learn) and model deployment workflows
- Experience in CI/CD tools (GitHub Actions, Jenkins, GitLab CI), scripting (Python, Bash), and API gateway management

📌 Senior Cloud Engineer (Delhi)
🏢 e-Solutions
📍 Delhi