Technical Architect - ML

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 & Qualifications: 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/CodePipeline or equivalent) Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, 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. Experience working on Infrastructure as Code (IaC) tools and CI/CD pipelines Experience with Kubernetes based development Experience with feature engineering pipelines and Feature Store management . Understanding of lineage tracking : training data snapshot, feature versions, code versioning, metadata tracking, reproducibility. Hands-on experience with AWS Bedrock and Agentcore service Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana. Strong foundation in Python and cloud-native