AI/ML Ops Engineer - MLOps

Job Description :

We are looking for an experienced AI/ML Ops Engineer to build, deploy, automate, and maintain machine learning and AI solutions in production environments. The candidate will be responsible for implementing reliable ML pipelines, deployment workflows, monitoring, and operational practices across the machine learning lifecycle.

Key Responsibilities :

- Design, build, and maintain end-to-end MLOps pipelines for machine learning and AI applications.

- Automate the training, validation, deployment, and monitoring of ML models.

- Develop and manage CI/CD pipelines for machine learning models and applications.

- Implement model versioning, experiment tracking, and reproducible ML workflows.

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

- Implement model monitoring, logging, performance tracking, and alerting.

- Manage data and model pipelines to ensure reliability, scalability, and consistency.

- Work with data scientists and ML engineers to productionize machine learning models.

- Implement infrastructure and deployment automation using Infrastructure as Code (IaC) and DevOps practices.

- Manage containerized ML workloads using Docker and Kubernetes.

- Ensure security, governance, and access controls across AI/ML environments.

- Troubleshoot production issues and optimize model serving and pipeline performance.

- Support scalable AI/ML infrastructure across cloud environments.

Required Skills :

- Strong experience in MLOps / AI Ops and machine learning lifecycle management.

- Hands-on experience with Python and scripting.

- Experience building CI/CD pipelines using tools such as Jenkins, GitHub Actions, or Azure DevOps.

- Experience with ML platforms and tools such as MLflow, Kubeflow, or equivalent.

- Strong knowledge of Docker and Kubernetes.

- Experience with cloud platforms such as AWS, Azure, or GCP.

- Experience with model deployment, monitoring, versioning, and lifecycle management.

- Good understanding of Git, REST APIs, Linux, and automation.

- Knowledge of data and ML pipeline orchestration tools.

- Strong troubleshooting and problem-solving skills.

Good to Have :

- Experience with LLMOps and Generative AI workloads.

- Experience with Terraform or other IaC tools.

- Knowledge of model serving frameworks and platforms.

- Experience with cloud-native AI/ML services.

- Understanding of model governance, security, and responsible AI practices.

- Experience working in Agile/Scrum environments.