AI DevOps Engineer
People Prime Worldwide
Date: 2 weeks ago
City: Vellore, Tamil Nadu
Contract type: Contractor
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
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
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