Machine Learning Engineer

Machine Learning Engineer

Remote Role : Alpharetta Georgia 30009

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 AI evidence engine

focused on LLM

2 rounds of Zoom interview 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