- Full Time
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Job Description Purpose
Provide leaders and employees with information about the functions and requirements needed to perform
the job. This must be read in conjunction with individual goals or competencies. This job description is used to
evaluate the job and provide information for external interviews and internal postings.
Provides a general summary of the job.
Understand the business problem, the data science solutions and operationalize it to deliver outcomes
at level of scale/efficiency, integrate with other systems. This role is mainly comprised of: Data
acquisition with a focus on CI/CD (continuous integration/continuous deployment), Model orchestration
& deployment, Governance and operational support.
Lists major actions performed in the job. Describes what occurs and the reason the action is taken. Provides
impact of each action by indicating percentage of time spent performing the functions. Regular and
predictable attendance is a required function of this position.
Description % of Time Spent
• Data Acquisition: Work with Data Engineering team to understand and help to
develop build-as-per-need infrastructure for Data collection and ETL processes,
automate steps in ETL & develop system to manage, deploy and maintain Data
Engineering code. Create data tools for analytics and data scientist team
members that assist them in building and optimizing our product into an
innovative industry leader. Work with data and analytics experts to strive for
greater functionality in our data systems (including feature engineering)
• Model orchestration & deployment: Assist in the development of systems to
manage, deploy and maintain ML code. Work closely with the Data Sciences
team to: Develop infrastructure in order for Machine Learning models to be
deployed, Take over newly developed models into production, Develop systems
for integrating AI/ML components using orchestration services. Build CI-CD
pipelines interconnecting Data services and ML services for the project with an
aim to achieve MLOps. Assist in development and implementation of ML
toolchains and data platforms to scale ML solutions in production.
• Governance and operational support: Enable the agility in data science delivery
through automation across build, validation, deployment and monitoring of
Data Science models. Monitor quality parameters for ML models in production.
Shape and operate best practices for managing models in production.
Contribute to solutions that accelerate the task of Production issue analysis by
Data Scientists by enabling log viewing tracing and debugging of data science
features in production.
Describes the minimum education and experience, certifications, licenses, physical demands, working
conditions and skill sets needed to perform the job.
Provide prompt, courteous and excellent service at an acceptable cost to all customers; operate in an
ethical manner in accordance with all applicable laws and regulations, the company’s Corporate Code of
Ethics, employee handbook, applicable compliance and operations policies and procedures, and other
policies of the company. Possess a high degree of integrity and actively cooperate and interact with all
entities of the Principal Financial Group.
• Bachelor’s Degree in Computer Science/Engineering, Informatics, or a related technical discipline
• 0-2 years of experience in below mentioned tools/technology
• Exposure to orchestration of machine learning services
• Exposure to building of CI/CD pipelines, data architecture and ML deployment
• Familiarity with machine learning frameworks (like TensorFlow, Keras, PyTorch, pyspark) and libraries
• Exposure to data solutions and cloud technology like Azure/AWS: Redshift, RDS, S3, Glue, Athena,
EMR, Spark, Hive, etc.)
• Knowledge of object-oriented/object function and scripting languages: Python, R, shell scripting, Java,
C++, Scala, etc
• Exposure to Cloud computing platform (AWS preferred)
This job reports to: Asst. Dir or Director – Machine Learning Engineer
Direct Reports: none