INTRODUCTION TO EVERNORTH:
The Evernorth Hyderabad Centre, established in 2024, supports Evernorth Health Services - the pharmacy, care and benefits division of The Cigna Group. The centre focuses on innovation-driven areas such as generative AI, product development, process improvement, analytics, and software engineering across The Cigna Group and its businesses. Evernorth Health Services India builds on The Cigna Group's existing presence in India.
About Evernorth Health Services:
Evernorth Health Services (Evernorth) is the pharmacy, care and benefits solution division of The Cigna Group - a Fortune 16 global health company with 75,000 employees, 186 million customers in more than 30 countries and jurisdictions around the world. Evernorth exists to make the prediction, prevention, and treatment of illness and disease more accessible to millions of people. We do this by creating and connecting premier health services offerings, such as benefits management, pharmacy, care solutions, insights and intelligence.
About Cigna:
The Cigna Group is a global health company committed to improving the health and vitality of individuals and communities around the world and includes products and services marketed under its Cigna Healthcare and Evernorth Health Services subsidiaries. Cigna Healthcare is the health benefits provider of The Cigna Group, serving customers and clients through its U.S. Employer, U.S. Government, and International Health business. Evernorth Health Services is the pharmacy, care and benefits solution division of The Cigna Group.
Position Overview
We're seeking a talented AI/ML Lead Analyst to join our healthcare AI engineering team and help build intelligent Prior Authorization (PA) solutions. In this role, you will design, develop, and deploy production-grade LLM-based applications, RAG pipelines, and agentic workflows that process clinical documents and support healthcare decision-making. You will work closely with clinical subject matter experts, product teams, and data scientists to build reliable, scalable, and secure AI solutions.
Roles & Responsibilities
- Design and deploy production-grade LLM-based applications using OpenAI GPT, Anthropic Claude and Azure models for healthcare prior authorization workflows
- Architect and optimize RAG pipelines for clinical document processing, achieving improved accuracy through multi-stage retrieval and context-aware prompting
- Build agentic AI workflows using LangChain/LangGraph for sequential and parallel task orchestration in healthcare decision support systems
- Lead model migrations and A/B testing across LLM providers (OpenAI ↔ Anthropic), implementing fallback mechanisms for production reliability
- Develop evaluation frameworks with automated accuracy tracking, token usage monitoring, and model performance benchmarking
- Drive cost and latency optimization through prompt engineering, token counting strategies, and efficient batching mechanisms
- Collaborate with cross-functional teams (clinical SMEs, data scientists, product managers) to translate healthcare requirements into scalable AI solutions
- Implement production monitoring and observability including real-time accuracy analysis, error tracking, and performance dashboards
- Stay current with AI/ML advancements and propose innovative solutions for complex healthcare workflows
Qualifications
Required Experience & Skills:
- 6+ years of experience in Python development, including building and deploying production-grade AI/ML systems in enterprise environments.
- 5+ years of hands-on experience building and deploying LLM-based applications, including prompt engineering, RAG, model evaluation, model selection, and LLM orchestration.
- Deep expertise in RAG architectures, including vector databases, embedding models, retrieval strategies, reranking, context optimization, and domain-specific information retrieval.
- Proficiency in agentic AI frameworks such as LangChain and LangGraph, with experience building multi-step workflows, tool orchestration, controlled execution, and AI-assisted decision-support systems.
- Strong production deployment expertise using FastAPI, Docker, Kubernetes/OpenShift, and CI/CD pipelines for AI/ML systems.
- Cloud AI/ML platform experience with Azure OpenAI Service, AWS SageMaker, Databricks, or equivalent enterprise AI platforms.
- Strong evaluation and monitoring skills, including LLM/RAG quality evaluation, accuracy measurement, A/B testing, token usage tracking, latency benchmarking, and automated quality monitoring.
- Proven experience with LLM cost and performance optimization, including prompt optimization, context reduction, model selection/routing, caching, and batch processing.
- Strong understanding of AI/ML architecture and software engineering principles, with the ability to translate business and healthcare requirements into scalable, secure, and maintainable AI solutions.
- Experience working with clinical SMEs, product teams, data scientists, engineering teams, and other cross-functional stakeholders.
- Experience working with sensitive healthcare data and understanding of data privacy, security, and compliance considerations for AI/ML applications.
Preferred Experience & Skills:
- Advanced degree (MS/PhD) in Computer Science, AI/ML, or related quantitative field
- MLOps and LLMOps expertise - model versioning, experiment tracking (MLflow, Weights & Biases), automated retraining pipelines
- Agentic AI and multi-agent systems - experience building autonomous AI workflows with tool use, planning, and self-correction capabilities
- Model migration and orchestration experience - seamless provider switching, load balancing across LLM endpoints, graceful degradation patterns
- Contributions to AI/ML open-source projects or peer-reviewed research publications in NLP/LLM domain
- Experience with evaluation frameworks - RAGAS, LangSmith, custom benchmark suites for RAG and LLM quality assessment
- Healthcare AI experience - clinical NLP, medical terminology extraction, or prior authorization domain knowledge
Equal Opportunity Statement
Evernorth is an Equal Opportunity Employer actively encouraging and supporting organization-wide involvement of staff in diversity, equity, and inclusion efforts to educate, inform and advance both internal practices and external work with diverse client populations.
