Senior AI Engineer

Overview

Role:
Senior Machine Learning / AI Engineer

Location: Onshore (U.S.)

The team has developed an intelligent search solution that includes mobile integration. The next phase focuses on extending member benefits and provider search capabilities to enable greater self-service, while leveraging AI to support intentional prompting and member decision-making
.

The selected candidate will play a key role in enhancing AI-driven features
, integrating advanced models, and strengthening the data and deployment pipeline across multiple platforms and channels.

• Design, develop, and implement AI/ML models and frameworks to enhance member experience and decision-making
  .
• Extend intelligent search functionalities with advanced natural language and retrieval capabilities
  .
• Build and manage ML pipelines connecting platform, AI, and channel teams (
  Dev Ops focus
  ).
• Implement Retrieval-Augmented Generation (RAG) and work with Large Language Models (LLMs) for conversational and decision-support systems.
• Apply Model Context Protocols (MCPs) and integrate Knowledge Bases for contextual and dynamic AI responses.
• Collaborate with cross-functional teams to embed AI-driven insights into digital member experiences.
• Serve as a liaison between the enterprise AI team and application/DHP teams to ensure smooth integration.
• Support onboarding, configuration, and integration of new AI components and models.
• Participate in testing, validation, and continuous improvement of deployed AI models and systems.
• Collaborate with data engineering teams to ensure AI models are integrated with data pipelines, data lakes, and enterprise systems
  .

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field
  .
• 3+ years of experience in Machine Learning, Artificial Intelligence, or related software engineering roles.
• Proven experience designing and coding AI/ML solutions end-to-end (Python, PyTorch, Tensor Flow, etc.).
• Strong understanding of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).
• Understanding of Knowledge Base architecture and integration into enterprise applications.
• Hands-on experience with Dev Ops practices for ML pipelines and model deployment (CI/CD, containerization, monitoring).
• Experience deploying AI solutions in cloud environments (Azure or AWS preferred).
• Familiarity with data storage, ETL processes, and integration with enterprise data systems.
• Excellent communication and collaboration skills
  , able to interface between technical and business teams.

• Experience in healthcare or member services domains.
• Familiarity with intelligent search systems, recommendation engines, or conversational AI
  .
• Knowledge of data governance, model security, and compliance best practices.
• Experience working in agile environments (Scrum, Kanban).
• Exposure to MLOps practices and cloud data pipelines
  .