MLOps Architect - AI Platform & Pipeline Ops

Job Description: You will be responsible for building and maintaining ML pipelines for training, validation, deployment, and monitoring. Additionally, you will implement CI/CD for ML artifacts, including data versioning and model registries. Monitoring model performance and drift to trigger retraining workflows as needed will also be part of your key responsibilities. Managing infrastructure (cloud, containerized, on-prem) for high-availability AI services and collaborating with data, ML, and ops teams to deliver a frictionless MLOps lifecycle are essential aspects of the role. Key Responsibilities: - Build and maintain ML pipelines for training, validation, deployment, and monitoring - Implement CI/CD for ML artifacts, including data versioning and model registries - Monitor model performance and drift; trigger retraining workflows as needed - Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services - Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycle Qualifications Required: - Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI) - Proficiency in Python, Bash, Docker, and Kubernetes - Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation) - Experience in model monitoring, logging, and alerting - Bonus: experience in BPS / regulated domains with compliance-aware deployment,