REQ ID: FEQ126R151

ML/MLOps Specialist Solutions Architect (Pre sales, DS/ML)

As a Specialist Solutions Architect (SSA), you will guide customers in building big data solutions on Databricks that span a large variety of use cases. These are customer-facing roles, working with and supporting the Solutions Architects, requiring hands-on production experience with Apache Spark™ and other big data technologies with a particular focus on the Data Science domain. SSAs help customers through the 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 to continuously consolidate the skills in your area of speciality.

Reporting to the Senior Manager, Field Engineering (Specialist)

The impact you will have:

• Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from architectural design to model deployment and operations
• Scale customer data science workloads, architecting new production level workloads, including end-to-end machine learning pipelines
• Build, scale and optimize customer data science workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains
• Support our customers solve critical data problems by leveraging your technical SME skills
• Improve community adoption by providing tutorials and training, including hackathons and conference presentations
• Assist Solutions Architects with in-depth aspects of the technical sales process, such as advanced custom proof of concepts, sizing complex workloads, and designing custom architectures
• Contribute to the Databricks Community and act as a technical SME.

What we look for:

• Pre-sales or post-sales experience working with external clients across a variety of industry markets
• You will have experience in a technical role involving the design, implementation, and operationalisation of Machine Learning models in production
• Passion for collaboration, life-long learning, and driving business value through ML
• Hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch
• Experience building production-grade machine learning solutions on AWS, Azure, or GCP
• Experience building Machine Learning solutions on cloud infrastructure and services, such as AWS, Azure, or GCP leveraging a strong understanding of:
• Model development including building, training, tuning, and evaluation processes
• Different types of ML algorithms and methods, including supervised and unsupervised machine learning, and Deep Learning methods
• MLOps concepts covering model monitoring, tracking, management, model serving & deployment, and other aspects of productionising ML pipelines in distributed data processing environments using tools like MLflow
• Ability to design highly performant, scalable, and cost-effective cloud-based data & ML solutions, such as distributed training and inference processes on GPU clusters.
• Experience with big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, and OLAP.
• Deep knowledge in development tools and best practices for engineers including CI/CD, unit and integration testing, and automation and orchestration
• Proven ability to maintain and extending production data systems to evolve with complex needs
• Strong programming experience in Python and potentially R
• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience
• This role can be remote, but we prefer that you are located in the job listing area and can travel up to 30% when needed