- Deploy and manage Machine Learning, AI, and Generative AI workloads across development, testing, and production environments.

- Build and maintain MLOps pipelines covering model packaging, deployment, versioning, validation, monitoring, and lifecycle management.

- Implement CI/CD processes for AI and ML applications using Azure DevOps, GitHub Actions, Jenkins, GitLab CI, or equivalent platforms.

- Manage containerized AI workloads using Docker and Kubernetes.

- Support AI services and ML platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, or MLflow.

- Monitor model performance, application health, infrastructure utilization, data quality, latency, failures, and production reliability.

- Implement logging, alerting, dashboards, and observability using tools such as Azure Monitor, Application Insights, Prometheus, Grafana, Splunk, or similar platforms.

- Automate deployment and operational activities using Python, Shell scripting, PowerShell, Terraform, or equivalent automation technologies.

- Support APIs, microservices, model endpoints, batch inference, and real time AI integrations.

- Troubleshoot AI application, model deployment, infrastructure, integration, and production issues.

- Support GenAI solutions including LLM endpoints, Retrieval Augmented Generation applications, vector databases, prompt workflows, and associated production monitoring.

- Implement appropriate model and application versioning, access controls, security, auditability, and operational governance.

- Work closely with Data Scientists,



AI Engineers, Data Engineers, DevOps teams, and application teams to productionize AI solutions.

- Maintain technical documentation, deployment procedures, operational runbooks, and incident resolution records.
- Required Technical Skills

- Solid understanding of AI/ML production environments and MLOps principles.

- Hands on experience with at least one cloud platform, Microsoft Azure, AWS, or Google Cloud Platform.

- Experience with AI/ML platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, or MLflow.

- Good programming and automation capability using Python, along with exposure to Shell scripting, Bash, or PowerShell.

- Experience with Docker and Kubernetes for containerized application deployment.

- Working knowledge of CI/CD and DevOps, using platforms such as Azure DevOps, GitHub Actions, Jenkins, or GitLab.

- Understanding of REST APIs, microservices, API integration, and model serving.

- Experience with monitoring and observability technologies such as Prometheus, Grafana, Splunk, Azure Monitor, or Application Insights.

- Exposure to Infrastructure as Code, preferably Terraform or equivalent.

- Understanding of databases, object storage, data pipelines, and cloud based integration patterns.

- Good knowledge of production troubleshooting, incident management, root cause analysis, and application support practices.

- Familiarity with Git based source control, branching, release management, and environment configuration.

📌 AI Ops Engineer (India)
🏢 Cognizant
📍 India