Description
Evernorth, a leading Health Services company, is looking for exceptional engineers/developers in our Data and Analytics organization.
In this role, you will actively participate with your development team on initiatives that support Evernorth's strategic goals as well as subject matter experts to understand business logic you will be engineering.
As a software engineer, you will help develop an integrated architectural strategy to support next-generation capabilities on an enterprise-wide scale.
You will work in an agile environment, delivering user-oriented products.
Roles & Responsibilities
- Design and build backend services for AI-enabled applications
- Integrate Large Language Models (LLMs) into enterprise systems
- Implement structured outputs, tool/function calling, and safe interaction patterns
- Develop and maintain APIs and service integrations
- Ensure systems are secure, observable, and production-ready
- Collaborate with product, architecture, security, and platform teams
- Lead technical design and review efforts
- Mentor and support other engineers
Qualifications
Required Experience & Education:
- 12-16 years of software engineering experience
- Strong experience with backend development (Python, or similar)
- Strong experience in LLM interaction
- Experience building and operating cloud-based systems (preferably AWS)
- Strong understanding of APIs, distributed systems, and service design
- Experience with authentication, authorization, and secure system design
- Familiarity with logging, monitoring, and production support
- Experience working with graph-based or relationship-centric data
Required Skills:
- GenAI, Python for integrating or working with AI/ML systems, Cloud Experience (AWS)
Preferred Skills, Experience & Education:
- Familiarity with LLM-based application patterns, prompts, responses and basic evaluation
- Understanding of responsible and controlled AI usage in production systems
Nice to Have:
- Experience with AI orchestration, workflows, or agent-based patterns
- Knowledge of semantic technologies or knowledge graphs
- Exposure to emerging AI integration standards or protocols
- Experience building internal platforms or shared services
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