Machine Learning Solutions Architect

Machine Learning Engineers are the Swiss army knives of machine learning. They’re ready for anything and they bring all the tools to ensure that data science models see the light of day. They own the infrastructure and deployment plan—from making sure data science models can actually be built using customer data to deploying them into a production environment and everything in between.  They provide thought leadership by recommending the right technologies and solutions for a given use case from the application layer to infrastructure.  Machine Learning Engineers have the team leadership and coding skills (e.g. Python Java and Scala) to get their solutions into production — and to help ensure performance security scalability and robust data integration.

As a Solutions Architect on our Machine Learning Engineering team you are responsible for:

• Designing and implementing data solutions best suited to deliver on our customer needs — from model inference retraining monitoring and beyond — across an evolving technical stack.

• Providing thought leadership by recommending the technologies and solution design for a given use case from the application layer to infrastructure; and they have the team leadership and coding skills (e.g. Python Java and Scala) to build and operate in production; and to help ensure performance security scalability and robust data integration.

What you’ll do in this role:

• Design and create environments for data scientists to build models and manipulate data

• Work within customer systems to extract data and place it within an analytical environment

• Learn and understand customer technology environments and systems

• Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop

• Demonstrate the business value of data by working with data scientists to manipulate and transform data into actionable insights

• Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models

• Partner with data scientists to ensure solution deployability—at scale in harmony with existing business systems and pipelines and such that the solution can be maintained throughout its life cycle

• Create operational testing strategies validate and test the model in QA and implementation testing and deployment

• Ensure the quality of the delivered product

This job might be for you if you bring...

• At least 6 years experience as a Machine Learning Engineer Software Engineer or Data Engineer

• 4-year Bachelor's degree in Computer Science or a related field

• Experience deploying machine learning models in a production setting

• Expertise in Python Scala Java or another modern programming language

• The ability to build and operate robust data pipelines using a variety of data sources  programming languages and toolsets

• Strong working knowledge of SQL and the ability to write debug and optimize distributed SQL queries

• Hands-on experience in one or more big data ecosystem products/languages such as Spark Snowflake Databricks etc.

• Familiarity with multiple data sources (e.g. JMS Kafka RDBMS DWH MySQL Oracle SAP)

• Systems-level knowledge in network/cloud architecture operating systems (e.g. Linux) and storage systems (e.g. AWS Databricks Cloudera)

• Production experience in core data technologies (e.g. Spark HDFS Snowflake Databricks Redshift & Amazon EMR)

• Development of APIs and web server applications (e.g. Flask Django Spring)

• Complete software development lifecycle experience including design documentation implementation testing and deployment

• Excellent communication and presentation skills; previous experience working with internal or external customers

You might also have...

• A Master’s or other advanced degree in data science or a related field

• Hands-on experience with one or more ecosystem technologies (e.g. Spark Databricks Snowflake AWS/Azure/GCP)

• Relevant side projects (e.g. contributions to an open source technology stack)

• Experience working with Data-Science and Machine-Learning software and libraries such as h2o TensorFlow Keras scikit-learn etc.

• Experience with Docker Kubernetes or some other containerization technology

• AWS Sagemaker (or Azure ML) and MLflow experience

• Experience building enterprise ML models

Why phData? We offer:

• Remote-First Work Environment

• Casual award-winning small-business work environment

• Collaborative culture that prizes autonomy creativity and transparency

• Competitive comp excellent benefits 4 weeks PTO plus 10 Holidays (and other cool perks)

• Accelerated learning and professional development through advanced training and certifications

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