Machine Learning Engineer

Remote Role : Alpharetta Georgia 30009 -100% REMOTE

W2 Candidates : with minimum validity of 12 months

Must have:

• Need good clinical and claims knowledge
• 7+ years of healthcare exp
• 4+ years of experience in machine learning, NLP, or deep learning
• They are working on an AI evidence engine
• focused on LLM
• 2 rounds of Zoom interviews then offer.

What You'll Do

• Develop, fine-tune, and optimize LLMs and modern deep learning models
• Write high-quality prompts, instructions, and training examples to shape model behavior
• Design, implement, and maintain instruction orchestration and evaluation workflows for LLM-based systems
• Build and maintain training pipelines, datasets, and evaluation workflows
• Design and execute functional and automated tests to validate AI outputs and system behavior
• Analyze model performance, identify failure patterns (e.g., accuracy gaps, hallucinations, edge cases), and drive improvements
• Collaborate with engineering and product teams (and review partner or vendor work) to deploy and iterate on AI features
• Contribute to the ongoing maintenance and improvement of existing AI systems

What We're Looking For

• 7+ years of experience in machine learning, NLP, or deep learning
• Hands-on experience with LLMs (GPT, LLaMA, Mistral, or similar) in applied or production contexts
• Healthcare data experience is required, including working knowledge of:
• Strong Python skills; experience with PyTorch or TensorFlow
• Familiarity with HuggingFace tools and modern model-training workflows
• Experience evaluating AI output quality, hallucination behavior, reliability, and consistency
• Experience designing automated evaluation, regression testing, or benchmarking pipelines for AI systems
• Ability to work with minimal direction, take ownership of problem areas, and operate effectively in ambiguous problem spaces
• Excellent communication skills for writing prompts, instructions, technical documentation, and evaluation artifacts
• Experience optimizing LLM and deep learning workloads on AWS, including model training, GPU utilization, and cost-efficient inference deployments