Sr Machine Learning Engineer

CVS Health•Irving, TX

2d•$112,923 - $185,400•Hybrid

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. CVS Shared Services Resources LLC, a CVS Health company, is hiring for the following role in Irving, TX: Sr Machine Learning Engineer to Design, develop, and implement machine learning models to solve business problems using structured and unstructured data. Build scalable ML pipelines that automate data ingestion, preprocessing, training, evaluation, and deployment. Develop software applications and tools to integrate ML models into production environments. Collaborate with data scientists and software developers to refine model requirements and translate prototypes into robust software solutions. Write clean, efficient, and well-documented code using programming languages such as Python, Java, or C++. Optimize ML models for performance and accuracy using hyperparameter tuning, feature selection, and model validation techniques. Deploy machine learning models to cloud or on-prem environments using tools like Docker, Kubernetes, and cloud platforms (e.g., AWS, Azure, GCP). Monitor and maintain model performance in production, implementing model retraining and version control as necessary. Conduct code reviews and adhere to software development best practices including CI/CD, version control, and unit testing. Develop APIs and microservices to serve ML models and support application integration. Perform data analysis and data engineering tasks including cleaning, transformation, and storage using SQL, Spark, or Pandas. Collaborate with cross-functional teams including product managers, analysts, and engineers to identify and prioritize ML use cases. Utilize and contribute to MLOps frameworks to manage the lifecycle of ML models efficiently. Ensure compliance with data privacy and security standards when handling sensitive datasets. Document machine learning systems, workflows, and decisions for internal knowledge sharing and regulatory requirements. Continuously research and evaluate new ML algorithms and technologies to improve model accuracy and system efficiency. Benchmark different algorithms for scalability and performance under different data and system conditions. Assist in defining the architecture of software systems involving machine learning components. Participate in sprint planning, stand-ups, and other Agile ceremonies to ensure timely and coordinated delivery of ML solutions. Remote work permitted but must live within commuting distance of designated office location and remain available to report to office as needed and required. Multiple openings.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Information Technology, Engineering, or a related field
Two (2) years of experience in the job offered or related occupation
Two (2) years of experience in CI/CD, Jenkins, GIT, or DevOps
Two (2) years of experience in XML, JSON, HTML, CSS, or JavaScript
Two (2) years of experience in Agile methodologies or SAFe Software Development Principles
Two (2) years of experience in Web Service APIs
Two (2) years of experience in Docker or Kubernetes
Two (2) years of experience in JIRA, Rally, or Confluence
Two (2) years of experience in Data analytics on large data sets in healthcare, business, or retail sector
Two (2) years of experience in Machine learning algorithms
Two (2) years of experience in Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation
Two (2) years of experience in Developing and deploying predictive models or ML systems in a cloud environment (GCP, AWS, or Azure)
Two (2) years of experience in Machine learning operations such as model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, and orchestration
Two (2) years of experience in Designing data architectures, including data pipelines, distributed computing engines, and machine learning infrastructure design

Responsibilities

Design, develop, and implement machine learning models to solve business problems using structured and unstructured data.
Build scalable ML pipelines that automate data ingestion, preprocessing, training, evaluation, and deployment.
Develop software applications and tools to integrate ML models into production environments.
Collaborate with data scientists and software developers to refine model requirements and translate prototypes into robust software solutions.
Write clean, efficient, and well-documented code using programming languages such as Python, Java, or C++.
Optimize ML models for performance and accuracy using hyperparameter tuning, feature selection, and model validation techniques.
Deploy machine learning models to cloud or on-prem environments using tools like Docker, Kubernetes, and cloud platforms (e.g., AWS, Azure, GCP).
Monitor and maintain model performance in production, implementing model retraining and version control as necessary.
Conduct code reviews and adhere to software development best practices including CI/CD, version control, and unit testing.
Develop APIs and microservices to serve ML models and support application integration.
Perform data analysis and data engineering tasks including cleaning, transformation, and storage using SQL, Spark, or Pandas.
Collaborate with cross-functional teams including product managers, analysts, and engineers to identify and prioritize ML use cases.
Utilize and contribute to MLOps frameworks to manage the lifecycle of ML models efficiently.
Ensure compliance with data privacy and security standards when handling sensitive datasets.
Document machine learning systems, workflows, and decisions for internal knowledge sharing and regulatory requirements.
Continuously research and evaluate new ML algorithms and technologies to improve model accuracy and system efficiency.
Benchmark different algorithms for scalability and performance under different data and system conditions.
Assist in defining the architecture of software systems involving machine learning components.
Participate in sprint planning, stand-ups, and other Agile ceremonies to ensure timely and coordinated delivery of ML solutions.