Teams at Quantiphi

Design and optimize enterprise data architectures, pipelines, and application workloads using SQL, Spark/PySpark, Python, Apache Iceberg, AWS, ClickHouse, and Aurora PostgreSQL. Build integrations with data virtualization and governance tools, support greenfield projects, optimize databases and Spark jobs, and collaborate on cloud, infrastructure-as-code, and CI/CD initiatives. Experience serving financial services and insurance customers is valuable, while telecommunications knowledge is preferred.

Design and develop advanced machine learning and generative AI solutions, including LLM fine-tuning, RAG architectures, prompt engineering, model evaluation, and MLOps. Architect scalable training, deployment, and retraining workflows on AWS using SageMaker and related services. Collaborate with engineering, QA, project management, and other stakeholders to deliver reliable AI applications and solve complex business problems.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.