Develop and manage pipelines for model promotion and other capabilities related to MDLC.
Optimize training throughput for large data sources
Establish and maintain integrations to platforms and tools related to model monitoring and observability
Collaborate with cross-functional teams to integrate new technologies and improve the capabilities of our ML Platform.
Partner with product, architecture, modeling, and engineering to design robust solutions that power our Digital channels
Required qualifications, capabilities, and skills
BS in Computer Science or related Engineering field with 6+ years of experience Or MS degree in Computer Science or related Engineering field with 4+ years experience.
Solid knowledge and extensive experience in Python and in cloud computing, along with ML frameworks (i.e. pytorch, tensorflow)
Deep knowledge and passion for data science fundamentals, training and deploying models
Experience in monitoring and observability tools to monitor model input/output and features stats
Operational experience in big data/ML tools such as Ray, Spark and in training/inference systems such as Ray, vllm/SGLang
Solid grounding in engineering fundamentals and enterprise system design
Preferred qualifications, capabilities, and skills
Experience with recommendation and personalization systems is a plus.
CUDA experience is a big plus
Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc]
Good knowledge of data storage solutions and strategies (online and offline)
