AI DevOps Engineer

We are looking for an experienced AI DevOps Engineer to build, automate, deploy, and manage scalable infrastructure and CI/CD pipelines for AI/ML applications. The candidate should have strong experience in DevOps, cloud platforms, containers, Kubernetes, MLOps, and AI/ML deployment.

Key Responsibilities

Design and implement CI/CD pipelines for AI/ML and software applications.

Automate build, testing, deployment, and release processes.

Deploy and manage AI/ML models across development, testing, and production environments.

Build scalable and reliable cloud infrastructure for AI workloads.

Manage Docker containers and Kubernetes clusters.

Implement MLOps practices for model training, deployment, monitoring, and lifecycle management.

Integrate machine learning workflows with CI/CD pipelines.

Manage infrastructure using Infrastructure as Code (IaC) tools such as Terraform.

Implement monitoring, logging, alerting, and performance optimization for AI applications.

Manage cloud services across AWS / Azure / GCP.

Implement security, access control, secrets management, and compliance practices.

Automate infrastructure and application deployment using Python, Bash, or PowerShell.

Collaborate with Data Scientists, ML Engineers, Software Developers, and Cloud Architects.

Troubleshoot production issues related to applications, infrastructure, containers, and ML workloads.

Optimize cloud infrastructure and AI workloads for cost, performance, scalability, and availability.

Required Skills

Strong DevOps / Cloud Engineering experience

CI/CD – Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps

Docker and Kubernetes

AWS / Azure / GCP

Terraform / Infrastructure as Code

Linux administration

Git and version control

Python / Bash scripting

Monitoring tools such as Prometheus, Grafana, ELK

Strong understanding of networking and cloud security

Experience with MLOps / AI model deployment

ML model lifecycle and productionization concepts

API deployment and microservices architecture

AI/ML & MLOps – Good to Have

MLflow

Kubeflow

TensorFlow / PyTorch

Hugging Face

LangChain / LLM application deployment

Model serving using KServe / Seldon

Vector databases

GPU/accelerated compute infrastructure

LLMOps / GenAI deployment and monitoring

Model monitoring and drift detection