Machine Learning Engineer

The AI Engineer is an ML engineer who is responsible for building and optimizing machine learning models, retrieval-augmented generation (RAG) systems, and verifiable ML systems that transform how we search and anproalyze complex medical and insurance data. This role requires a deep understanding of machine learning, LLM technologies, data science, and software engineering. This is an ML-engineering role and not a data science role, which means you should be comfortable building production-ready systems. This is not a research role.

ESSENTIAL JOB RESPONSIBILITIES & KEY PERFORMANCE OUTCOMES :

• Data engineering: Design and optimize systems capable of processing large volumes of data in both batch and real-time.
• Design, implement, and optimize retrieval-augmented generation (RAG) systems that leverage the latest LLM technologies to deliver highly relevant search results at scale.
• Develop and refine embedding models, vector databases, and retrieval mechanisms that maximize search relevance while minimizing latency and operational costs.
• Create robust evaluation frameworks to measure quality, continuously improve our LLM-based solution based on user feedback and performance metrics.
• Build and optimize machine learning models and verifiable ML systems that transform how we search and analyze complex medical and insurance data.
• Build and optimize production-ready systems that are scalable and can handle large amounts of data.
• Build and optimize agentic systems that can reason and make decisions.
• Develop machine learning models in healthcare and insurance settings with the highest standards for safety and quality.
• Stay on top of AI and ML security and governance requirements.

REQUIRED QUALIFICATIONS :

• BS in Computer Science (concentration on machine learning/AI), Engineering, Statistics, or a related field
• At least three (3) years of industry experience and specifically with some of the recent Gen AI technologies.
• Experience with containerization, Kubernetes, and cloud-native ML technologies such as Kubeflow.
• Experience with CI/CD pipelines and automated testing.

High level of proficiency with Python and several ML frameworks.