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From the employer's careers site First seen 29 Oct 2026 · Still live on 1 Nov 2026

Application Development Associate Principal-AI/Machine Learning

Evernorth Hyderabad, India senior full-time

The employer hasn't listed a salary.

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Description

Evernorth exists to elevate health for all, because we believe health is the starting point for human potential and progress.

We're seeking a talented AI/ML Engineer to design, develop, and deploy cutting-edge AI and machine learning solutions.

Responsibilities

  • Design and implement AI/ML models using various frameworks and libraries
  • Work with large language models (LLMs) from providers like OpenAI and Anthropic
  • Develop and optimize RAG (Retrieval-Augmented Generation) systems
  • Build scalable ML pipelines using Databricks, MLflow, and cloud-native tools for model training, tracking, and deployment
  • Collaborate with cross-functional teams to integrate AI capabilities into our applications
  • Conduct experiments and analyze results to improve model performance
  • Stay updated with the latest advancements in AI/ML and propose innovative solutions

Qualifications

  • 14 to 18 years of development experience
  • Strong proficiency in Python and AI/ML libraries (e.g., PyTorch, TensorFlow)
  • 10+ years of experience working with Artificial Intelligence, experienced working on LLM & latest AI tools
  • Familiarity with RAG systems and related technologies
  • Strong understanding of machine learning concepts and algorithms
  • Experience with cloud-based ML platforms (e.g., AWS SageMaker, Databricks)
  • Proficiency in data preprocessing and feature engineering

Preferred Experience & Skills

  • Advanced degree in Computer Science, AI, or a related field
  • Experience with graph databases (e.g., Neo4j) and knowledge graphs
  • Familiarity with MLOps practices and tools
  • Experience with GPU acceleration and distributed computing
  • Contributions to AI/ML open-source projects or research publications

Skills

Required

Nice to have

  • Graph databases
  • Knowledge graphs
  • MLOps practices
  • GPU acceleration
  • Distributed computing
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