Machine Learning Associate Advisor - HIH - Evernorth

Cigna — Hyderabad, India

About Evernorth: Evernorth Health Services, a division of The Cigna Group (NYSE: CI), creates pharmacy, care, and benefits 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. Title: Machine Learning Associate Advisor Position Summary: We value our talented employees, and whenever possible strive to help one of our associates grow professionally before recruiting new talent to our open positions. If you think the open position you see is right for you, we encourage you to apply! Our people make all the difference in our success. As a AI Prompt Engineer, you will be joining a team transforming healthcare and improving the lives and vitality of the millions of members we serve. We leverage cutting edge Artificial Intelligence (AI) and Machine Learning (ML) algorithms to develop solutions for automated document processing and customer service chat bots. We are looking for AI Prompt Engineers with strong engineering, full stack expertise to build the best fit solutions leveraging Large Language Models (LLMs) and Generative AI solutions. Extreme focus on speed to market and getting Products and Services in the hands of customer and passion to transform healthcare is key to the success of this role. Job Description & Responsibilities: Build scalable software solutions using LLM’s and other ML models to solve challenges in healthcare. Experience in implementing enterprise systems in production setting for AI, computer vision, natural language processing. Exposure to self-supervised learning, transfer learning, and reinforcement learning is a plus. Build enterprise grade AI solutions with focus on privacy, security, fairness. Work with Product Development as a Generative Artificial Intelligence (AI) subject matter expert and architect and develop scalable, resilient, ethical AI solutions Strong engineering skills to design the output from th

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