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.
Troubleshoot and resolve issues related to model deployment and performance.
Ensure compliance with security and data privacy standards in all MLOps activities.
Keep up-to-date with the latest MLOps tools, technologies and trends.
Provide support and guidance to other team members on MLOps practices.
Required Skills and Experience:
5+ years of experience in MLOps, DevOps or a related field.
Strong understanding of machine learning principles and model lifecycle management
Experience in implementing ML model monitoring techniques and tools
Proficiency in programming languages such as Python, PySpark, Spark/Scala
Hands-on experience in machine learning and deep learning frameworks like Spark ML, TensorFlow, PyTorch, Scikit-learn
4+ years of solution architecting in cloud technology (eg. AWS, Azure, google cloud) and their respective machine learning services
4+ years of working experience with operationalizing Machine learning models
4+ years being part of Agile teams – Scrum or Kanban
About Evernorth Health Services
Evernorth Health Services, a division of The Cigna Group, creates pharmacy, care and benefit solutions to improve health and increase vitality. We relentlessly innovate to make the prediction, prevention and treatment of illness and disease more accessible to millions of people. Join us in driving growth and improving lives.
Skills Required
- 5+ years of experience in MLOps, DevOps or a related field.
- Strong understanding of machine learning principles and model lifecycle management
- Experience in implementing ML model monitoring techniques and tools
- Proficiency in programming languages such as Python, PySpark, Spark/Scala
- Hands-on experience in machine learning and deep learning frameworks like Spark ML, TensorFlow, PyTorch, Scikit-learn
- 4+ years of solution architecting in cloud technology (eg. AWS, Azure, google cloud) and their respective machine learning services
- 4+ years of working experience with operationalizing Machine learning models
- 4+ years being part of Agile teams - Scrum or Kanban
- Experience with Databricks and AWS for building and deploying analytics workflows
- Automation-first mindset and experience driving CI/CD adoption
- Familiarity with security and data privacy standards for MLOps activities
Cigna Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cigna and has not been reviewed or approved by Cigna.
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Strong & Reliable Incentives — Strong bonus outcomes are frequently highlighted, with annual bonuses described as really good alongside above-average salary levels. Stock or long-term incentive elements are also noted as part of the overall package in some roles.
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Leave & Time Off Breadth — Time-off benefits are portrayed as a meaningful part of total rewards, including generous PTO and flexibility that can enhance the perceived value of compensation. Flexible work-from-home arrangements are repeatedly linked with satisfaction about the overall package.
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Healthcare Strength — Health coverage is described as broad in design, with preventive care often covered at no charge in-network and options like virtual care and wellness incentives. A large provider network and strong digital tools are positioned as practical advantages when using benefits.
Cigna Insights
What We Do
At Cigna, we're more than a health insurance company. We are your partner in total health and wellness. And we’re here for you 24/7 – caring for your body and mind. As a global health service company, Cigna's mission is to improve the health, well-being, and peace of mind of those we serve by making health care simple, affordable, and predictable. Our values are the core of our culture. Our values guide how all 74,000 of us around the world work together, serve our customers, patients, clients, communities, and deliver on our mission.
