While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Must have skills &
8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure. Strong expertise in AWS cloud-native ML stack , including: SageMaker(primary), EKS, Lambda, API Gateway, CI/CD (CodeBuild/Code
Pipeline or equivalent) Hands-on experience with at least one major MLOps toolset and awareness of alternatives: M
Lflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, K
Serve, Seldon. Deep understanding of model lifecycle management (feature engineering->training → registry → deployment → monitoring). Experience implementing or supporting LLMOps pipelines , including: prompt versioning, evaluation metrics, automation frameworks Deep understanding of ML lifecycle : data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor). Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment. Experie