Job Description
Machine Learning Solutions Architect
phData
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, generous weeks PTO plus 10 Holidays (and other cool perks)
Accelerated learning and professional development through advanced training and certifications
phData
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, generous weeks PTO plus 10 Holidays (and other cool perks)
Accelerated learning and professional development through advanced training and certifications
About phData
phData is the leading AI and data services company. We specialize in AI and data applications, from conception to production. Our global delivery team partners with the world's top brands to execute data initiatives in artificial intelligence, data engineering, applications, analytics, and managed services for cloud platforms.
Industry: IT Services and IT Consulting