Job Details

AI/ML Engineer

Long-term Contract

Overview

Our Fortune 50 Healthcare Insurance client is seeking a highly skilled AI/ML Engineering resource to play a pivotal role in the development, expansion, operation, and maintenance of generative AI solutions. The primary responsibilities include running an experimental framework to determine the optimal prompt engineering approaches, tuning prompts, and collaborating with subject matter experts (SMEs) for evaluations and results. This role requires a deep understanding of evaluating models output in production, particularly when ground metrics are absent, monitoring for issues such as model drift and hallucinations, and optimizing for offline and online metrics.

Key Responsibilities

• Scope, develop, expand, operate, and maintain scalable, reliable and safe generative AI solutions.
• Design and execute prompt engineering experiments to optimize Large Language Models (LLMs) for various use cases.
• Collaborate with SMEs to evaluate prompt effectiveness and align AI solutions with business needs.
• Understand and apply offline and online evaluation metrics for LLMs, ensuring continuous model improvements.
• Evaluate production models using live data in the absence of ground metrics, implementing robust monitoring systems.
• Monitor LLM applications for model drift, hallucinations, and performance degradation.
• Ensure smooth integration of LLMs into existing workflows, providing real-time insights and predictive analytics.

Qualifications

• Solid experience in business analytics, data science, software development, and data engineering.
• Expertise in Python and frameworks such as PyTorch, TensorFlow, or ONNX.
• Hands-on experience working with LLMs and Generative AI, including model development and inference patterns.
• Proven ability to design scalable systems leveraging LLMs, particularly in distributed computing environments.

Required Skills

• Experience in prompt engineering and prompt optimization.
• Expertise in running experiments to evaluate generative AI performance.
• Knowledge of production-level monitoring tools for ML models, including drift detection and mitigation strategies.
• Excellent problem-solving skills and ability to work cross-functionally with data scientists, engineers, and SMEs.
• Experience with safety, security and responsible use of AI.
• Experience with red-teaming (adversarial testing) of generative AI.
• Experience with developing AI applications with sensitive data such as PHI, PII and highly confidential data.