Data Scientist/Applied AI Engineer (3)

Data Scientist/ AI ENGINEER {3}
6-month contract with extension 
Location:

• New York-NY, metropolitan area;
• San Francisco-CA, metropolitan area;
• Austin-TX metropolitan area;  

{REMOTE-Hybrid} primary with commutable distance to a hub
RATE $125.00-$140.00 w2

Overview:
We are seeking an Applied AI Engineer who can build and operate production-grade AI systems that turn machine learning capabilities into reliable solutions for healthcare workflows. This is a hands-on AI engineering role with a strong emphasis on Python, PyTorch, LLM applications, and production AI systems. You will work across AI pipelines, model evaluation, structured data, APIs, async workflows, testing, observability, and healthcare use cases such as clinical documentation, coding, claims, denials, and revenue cycle automation. The ideal candidate is not simply focused on experimentation or research—they understand how to take AI capabilities from development into dependable, measurable, and auditable production systems.

Requirements:

1. Must have demonstrated experience building and operating production AI/ML systems, beyond notebooks, prototypes, or research experiments.
2. Must have strong Python experience developing production systems, including APIs, async workflows, structured data processing, testing, logging, and observability.
3. Must have hands-on PyTorch experience, including the ability to demonstrate practical coding and ML implementation skills.
4. Strong understanding of machine learning fundamentals, including model development, evaluation, training concepts, and common ML approaches.
5. Demonstrated experience with at least one LLM application technology or workflow, such as agent frameworks, tool calling, RAG/retrieval, structured outputs, prompt engineering, or model evaluation.
6. Experience building AI evaluation, benchmarking, annotation, regression testing, or model validation systems to measure and improve AI performance.
7. Demonstrated ability to translate complex real-world workflows into structured AI problems such as classification, ranking, extraction, prediction, or decisioning.
8. Demonstrated breadth across AI engineering, software development, evaluation, infrastructure, and production operations rather than specialization in only one narrow area.
9. Experience with healthcare workflows including clinical documentation, medical coding, claims, denials, payer policy, or revenue cycle management (RCM) is strongly preferred.
10. Experience with fine-tuning, supervised fine-tuning, reward modeling, distillation, reinforcement learning, or synthetic data generation is preferred.