Software Engineering Associate Advisor – HIH – Evernoth

Key engineering leading POPS :(pipeline operation system) automated framework to build and deploy analytics workflows in databricks and AWS. This role will work on MIME: Enterprise-wide entity that intakes and govern all enterprise wide analytical workflows making sure our analytical workflows are not unfairly targeting certain demographics (race, gender and zip codes)

Software Engineer Advisor (MLOps Engineer)

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production environments. This role bridges the gap between data science and IT operations, ensuring seamless integration of machine learning models into operational workflows. The MLOps Engineer works closely with data scientists, software engineers and DevOps teams to automate and streamline the model lifecycle, from development to deployment and monitoring.

In addition to Delivery, the MLOps Engineer should have an automation first and continuous improvement mindset. They should drive the adoption of CI/CD tools and support the improvement of the tools sets/processes.

Behaviors of MLOps Engineer:

Full Stack Engineers is able to articulate clear business objectives aligned to technical specifications and work in an iterative, agile pattern daily. They have ownership over their work tasks, and embrace interacting with all levels of the team and raise challenges when necessary. We aim to be cutting-edge engineer – not institutionalized developers.

This position is with Evernorth, a new business within the Cigna Corporation

Key duties and responsibilities:

- Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models.

- Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes.

- Automate the training, testing and deployment processes for machine learning models.

- Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.

- Implement best practices for version control, model reproducibility and governance.

- Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness.