AI Infrastructure Engineer (DevOps/MLOps)

You will design and manage CI/CD pipelines for software applications and machine learning workflows, deploying and monitoring ML models in production using tools like MLflow, SageMaker, or Vertex AI. Automation of infrastructure provisioning and configuration through IaC tools such as Terraform or Pulumi will be a key aspect of your role. Building robust monitoring, logging, and alerting systems for AI applications, alongside managing containerized services with Docker and Kubernetes, will be essential for maintaining system reliability and scalability.

Collaboration with data scientists and ML engineers to streamline model experimentation, versioning, and deployment will be a part of your daily tasks. Optimizing compute resources and storage costs across cloud environments like AWS, GCP, or Azure, and ensuring system security will also fall under your purview.

To excel in this role, you should have at least 5 years of experience in DevOps, MLOps, or infrastructure engineering roles, with hands-on experience in cloud platforms and services related to ML workloads. Proficiency in CI/CD tools, Docker, Kubernetes, and infrastructure-as-code frameworks is essential. Scripting skills in Python, Bash, or similar languages for automation tasks, along with familiarity with monitoring/logging tools, are highly valued.

Preferred qualifications include experience with Kubeflow, MLflow, DVC, or Triton Inference Server, as well as exposure to data versioning, feature stores, and model registries. Certification in AWS/GCP DevOps or Machine Learning Engineering is a plus, and a background in software engineering, data engineering, or ML research is considered a bonus.

By joining Techvantage.ai, you will have the opportunity to work on cutting-edge AI platforms and infrastructure, collaborate with top ML, research, and product teams, and receive a competitive compensation package with no constraints for the right candidate.,