Specialist Solutions Architect - HLS

FEQ325R86

We are open to hiring in any remote location in the USA. 

As a Specialist Solutions Architect (SSA) - Machine Learning on the Healthcare & Life Sciences team, you will guide customers in building big data solutions on Databricks that span a large variety of machine learning use cases. You will be in a customer-facing role, working with and supporting Solution Architects, that requires hands-on production experience with MLFlow™ and expertise in other MLOps technologies. SSAs help customers through design and successful implementation of essential workloads while aligning their technical roadmap for expanding the usage of the Databricks Lakehouse Platform. As a deep go-to-expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in an area of specialty - whether that be machine learning, MLOps, industry expertise, or more.

The impact you will have:

• Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from feature engineering, training, tracking, registry, serving to model monitoring all within a single platform
• Architect production level workloads, including end-to-end ML pipelines load performance testing and optimization
• Become a technical expert in Databricks Machine Learning and MLOps technologies
• Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing, and custom architectures
• Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
• Contribute to adoption of a variety of the ML offerings Databricks with customers as well as the larger Databricks Community

What we look for:

• 5+ years hands on experience in a technical role with expertise in at least one of the following:
• Data Scientist/ML Engineer: model selection, model lifecycle, model scaling, AutoML, hyperparameter tuning, model serving, model monitoring, deep learning
• MLOps Engineer: Build and maintain cloud infrastructure that supports the deployment of ML models and algorithms, monitors data drift, integration with production systems
• Extensive experience in applying Data Science / ML in production to build data-driven products for solving business problems
• Experience maintaining and extending production data systems to evolve with complex needs
• Deep Specialty Expertise regarding ML concepts including Model Tracking, Model Serving and other aspects of productionizing ML pipelines in distributed data processing environments like Apache Spark, using tools like MLflow
• Production programming experience in SQL and Python, Scala, or Java
• 2 years hands-on professional experience with …