Senior Machine Learning Operations Engineer

About the role

At CVS Health, we're building a world of health around every consumer and surrounding ourselves with dedicated colleagues who are passionate about transforming health care.As the nation's leading health solutions company, we reach millions of Americans through our local presence, digital channels and more than 300,000 purpose-driven colleagues - caring for people where, when and how they choose in a way that is uniquely more connected, more convenient and more compassionate. And we do it all with heart, each and every day.An exciting journey is just beginning-continue reading to find out more!At CVS, we leverage machine learning to effectively address customer needs and enhance the shopping experience by offering a broad selection of products that cater to various preferences and lifestyles. By harnessing our comprehensive ExtraCare® data and advanced predictive analytics, we forecast shopper preferences, market trends, and seasonal demands, ensuring that we stock in-demand and relevant items while optimizing inventory levels to boost sales and minimize waste. Our data-driven strategy not only enhances product availability but also drives sales performance, allowing CVS to successfully cater to the varied needs of our consumers while maximizing brand engagement.Roles and Responsibilities: Partner closely with data scientists to understand model specifications, driving a seamless transition for model launch from development to production. Create and manage continuous integration and continuous deployment (CI/CD) pipelines for machine learning models to automate the deployment process. Oversee the end-to-end lifecycle of machine learning models, including development, validation, deployment, monitoring, and retraining. Track model performance using relevant metrics and techniques, such as accuracy, and precision score, to ensure alignment with business objectives and address issues related to model drift and data quality. Conduct performance analysis and optimization through techniques like hyperparameter tuning and resource allocation strategies. Create and maintain automated workflows for model training and inference to enhance efficiency. Optimize the underlying infrastructure for machine learning operations, including cloud services and container orchestration, to ensure scalability and performance. Establish version control practices for models, datasets, and code to ensure reproducibility and facilitate collaboration. Conduct code reviews and contribute to the development of engineering frameworks and best practices for model deployment and maintenance. Develop and maintain comprehensive documentation for model deployment processes, architecture, and workflows. Perform root cause analysis for model failures and implement corrective actions to improve reliability. Offer support for troubleshooting and resolving issues related to model performance and infrastructure. Oversee and optimize cloud resources for cost-effective model training and deployment. Ensure adherence to security best practices and data privacy regulations in all machine learning operations. Mentor and guide junior engineers, providing support in their professional development and helping them enhance their technical skills. Required Qualifications: 5+ years of experience in the IT Industry, with at least 2 years specifically focused on Machine Learning, showcasing a strong grasp of machine learning principles alongside data engineering concepts. Strong programming skills in languages such as Python or R, along with experience in using machine learning libraries and frameworks like TensorFlow, PyTorch, or Scikit-learn for model development and evaluation. Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, to effectively implement and optimize models in production environments. Proficiency in cloud platforms such as Azure or Google Cloud Platform (GCP), with hands-on experience in utilizing their machine learning services and tools for model deployment and management. Experience with orchestration tools like Kubeflow and Apache Airflow for managing machine learning workflows and data pipelines. Experience with containerization technologies like Docker and orchestration tools such as Kubernetes, enabling efficient deployment, scaling, and management of machine learning applications. Knowledge of CI/CD practices and tools, including Jenkins, GitLab or GitHub to automate the deployment and testing of machine learning models, ensuring rapid and reliable updates. Understanding of monitoring and logging tools (e.g., Prometheus, or Grafana) to track model performance and system health, allowing for proactive management and troubleshooting. Familiarity with data processing frameworks such as Pandas, Dask, and Spark, which are essential for handling large datasets and performing distributed data processing in cloud environments. Excellent pr

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