Machine Learning Architect

Key Skills

Algorithms, Apache Airflow, API, Business Development, Data Classification, Data pipelines, Data science techniques, Deep Learning, Digital Transformation, Docker, Flask, Flight Data Monitoring-FDM, Generalization, IT Security, JavaScript, Keras software library, Kubeflow, Kubernetes-K8s, Large scale deployment, Machine learning techniques, MLflow, MLOps tools, Neural Networks, Prototypes, Python Programming, PyTorch, Regression Analysis, RESTful, SPARK Programming, Synchronization, TensorFlow, Time series

Job Description

Synchronoss, the leader of cloud, messaging and digital transformation to Telecommunications Media Technology companies. We transform the way companies create new revenue, reduce costs and delight their subscribers with cloud, messaging and digital transformation products. Synchronoss today supports hundreds of millions of subscribers across the globe. Synchronoss’ secure, scalable and groundbreaking new technologies, trusted partnerships and incredible talent change the way TMT customers drive and grow their business.

We’re looking for a Machine Learning Architect to join our current analytics team. As an Architect, you will have a fantastic team of Data Scientists and Data Engineering team to support you, a collaborative environment to encourage your work, and the best technologies for implementing data science at scale. You will liaise with the analytics platform team to recommend, evaluate and implement modern Machine Learning Platform to launch exciting product features.

How you will help:

• Collaborate with Product Managers, Data Scientists and other architects to design end-to-end Machine Learning Solutions
• Employ AI/ML architectural knowledge, MLOps standards to achieve Data Science objectives
• Evaluate and Implement key ML components: Feature store, Model Serving, Pipelines, Model Registry, Monitoring
• Apply & scale machine learning algorithms to build new product features
• Develop, Prototype and present full lifecycle ML Solutions for business use cases
• Support MLOps and resolve any issues during the entire Model Lifecycle
• Guide machine learning architectural design decisions and ensure high availability, performance, and quality of our applications

Who we have in mind:

• 5+ years of experience working in large scale data science environments
• 2+ years of experience in technical architecture, deploying ML models to production
• Experience with key ML Platform Technologies: Kubernetes, Kubeflow, MLFlow, Airflow, Spark, Docker
• Hands-on experience deploying and managing models at scale, including governance and security
• Experience working with RESTful API and general service-oriented architectures.
• Hands-on software development experience in Java, Python, Flask
• Fluent with variety of data sources, building data/ML pipelines
• Experience with TensorFlow, Keras, Pytorch, and other deep learning frameworks is a plus

It would be great if you had:

• Working Knowledge of various algorithms (Regression, Classification, Time Series, Neural Nets …)
• You care about code simplicity and performance
• You have a BS/MS in a scientific field or equivalent experience

What we offer:

• Competitive Salary + Bonus
• Flex Time PTO
• Healthcare
• 401K
• Vision
• Dental