Job Description

Your Role Design and implement scalable, cloud-native MLOps infrastructure to support the entire ML lifecycle, from experimentation to production deployment Help develop IAC scripts for required Infra provisioning Establish best practices for ML infrastructure monitoring, logging, and observability Collaborate with MLOPS pipeline developers to understand their workflows and optimize infrastructure accordingly Drive technical decisions around tooling selection and infrastructure architecture Mentor junior engineers and provide technical leadership to the MLOps team Your Background Key ingredients for succeeding in this role are your: 7+ years of software engineering experience, with at least 3 years focused on ML/AI infrastructure Strong experience with cloud platforms (AWS/GCP/Azure) and container orchestration (Kubernetes) Expertise in infrastructure-as-code using tools like Terraform, CloudFormation Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, etc.) Deep understanding of ML workflows, including training, experimentation, and deployment Experience with MLOps tools (MLflow, Kubeflow, Seldon, etc.) and CI/CD platforms (Jenkins, GitLab CI, etc.) Strong background in distributed systems and scalable architecture design Experience with monitoring and observability tools