Specialist Solutions Architect - MLOps

Key Skills

Aartificial intelligence, Apache Cassandra, Apache Hadoop, AWS, Azure, Big Data Technology, Cloud computing, Customer Representative, Data science techniques, Google Cloud Platform (GCP), Java Programming, Machine learning techniques, MLflow, MLOps tools, Python Programming, R Programming, SaaS, Sales, Spark Core

Job Description

This role can be remote.

As a Specialist Solutions Architect (SSA) - Machine Learning, 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 experience in a customer-facing 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
• Maintain and extend production data systems to evolve with complex needs
• Production programming experience in Python, R, Scala or Java
• 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
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Ability to travel up to 30% when needed

Benefits

• Comprehensive health coverage including medical, dental, and vision
• 401(k) Plan
• Equity awards
• Flexible time off
• Paid parental leave
• Family Planning
• Gym reimbursement
• Annual personal development fund
• Work headphones reimbursement
• Employee Assistance Program (EAP)
• Business travel accident insurance
• Mental wellness resources

About Databricks

Databricks is the data and AI company. More than 9,000 organizations worldwide — including Comcast, Condé Nast, and over 50% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems.