Position Summary: 

If you’re eager to make a real impact in the health care industry through your own meaningful contributions, explore a role in technology with CVS Health. Our journey calls for technical innovators and data visionaries: come help us pave the way. 

As a Senior Machine Learning Engineer, you will design, develop, and operationalize highly complex and large-scale Machine Learning Solutions on the Google Cloud Platform. In addition to the traditional machine learning toolset, you will work with large language models, vector stores, deep learning, and other advanced tools to handle, analyze, and generate insights from large volumes of data. 

You will be working with a mature data science organization at a Fortune 6 company. You will be responsible for operationalizing the analytical workloads and ML Pipelines using GCP AI/ML Services to support traditional ML use cases, as well as NLP, NLM, audio or image processing. You will support the deployment of advanced algorithms and applications, follow architecture and engineering best practices, and deliver software that is well documented, error free, scalable and performance optimized. 

As a Senior Machine Learning Engineer, you will:

• Work closely with data scientists, software developers and product managers to take ML-powered services from inception to production.
• Lead exploration, design and execution of machine learning models and frameworks that deliver value to our users.
• Build data pipelines and leverage high-quality datasets to power your Machine learning models.
• Utilize modern software and data-engineering stacks to enable training, deployment, and lifecycle management of ML models.
• Lead the performance and ongoing enhancements of your products.

Required Qualifications:  

• 5+ years' experience designing and deploying ML models.
• 5+ years' experience developing production-level software, or contributing to open source with one or more modern languages, such as Python, Java, etc.
• 3+ years of experience with common ML libraries (i.e., Scikit-learn, XGBoost, NumPy, Keras, PyTorch, etc.) and ML algorithms (i.e., clustering, decision trees, boosting, etc.)
• 3+ years of experience with developing Analytical pipelines (using languages such as SQL and PySpark) against Cloud Data Warehouses such as BigQuery, Snowflake or Redshift
• 1+ year experience in cloud environments (GCP preferred): bucket storage, BigQuery, Kubeflow, DataProc, VertexAI, DataFusion 

Preferred Qualifications:  

• 1+ year(s) of soliciting complex requirements and managing relationships with key stakeholders.
• 1+ year(s) of experience independently managing deliverables.
• 1+ year(s) of experience with open-source experience with either Docker or Kubernetes.
• Experience with complex systems and solving challenging analytical problems 
• Effective written and verbal communication skills 
• Experience with ML operationalization and data and model lifecycle management 
• Experience deploying and monitoring analytical assets in batch/real-time business processes 
• Understanding of DevOps principles and tools (such as GitHub, Jenkins, Circle CI, Argo CD, Artifact Registry, Nexus)  
• Experience in developing distributed data processing pipelines using libraries like Apache Spark or Beam. 
• Understanding of dependency management and containerization of applications supporting analytical workloads (i.e. Dockers, GKE) 
• Exposure to developing RESTFUL APIs against enterprise data assets and features to support real time operational ML pipelines. Experience with FastAPI or Flask is preferred. 
• Experience in data visualization tools and libraries 
• Formal SAFe and/or agile experience. Previous healthcare experience and domain knowledge 

Education: 

• Bachelor’s Degree or equivalent work experience in Computer Science, Information Systems, Data Engineering, Data Analytics, Machine Learning, or related field required.
• Master’s Degree preferred.