AI Project Manager

Omega•United States,

5d•Remote

About The Position

The AI Project Manager will lead the end-to-end delivery of our auto-coding AI engine. This position will sit at the intersection of clinical operations, data science, and engineering, ensuring that our specialty specific AI models move seamlessly from research and development to production. The AI Project Manager requires a deep understanding of the iterative nature of ML models, data labeling bottlenecks, and the nuances of Agentic AI performance tracking.

Requirements

Bachelor’s degree in business, computer science, or a related field.
10+ years in Project or Program Management, with at least 3 years dedicated specifically to AI/ML/GenAI/AgenticAI products.
Expert proficiency in Agile/Scrum, with the ability to adapt these frameworks for the experimental nature of data science.
Strong understanding of ML concepts (e.g. training vs. inference, precision/recall, fine-tuning, and the basics of Agentic AI).
Proficiency in Jira, Confluence, and ML-specific tracking tools (like Weights & Biases or MLflow).

Nice To Haves

PMP or Agile certifications are a plus.
Direct experience managing health-tech projects or RCM software implementations.
Experience managing large-scale data labeling/annotation projects (either internal or with vendors).
Experience leading organizational change when transitioning from manual human processes to AI-automated workflows.

Responsibilities

Oversee the full AI/ML lifecycle, including data acquisition, annotation, model training, evaluation, and production deployment.
Manage and deliver solutions using GenAI components and leverage advanced techniques to get the best output/accuracy out of LLMs/SLMs.
Monitor KPIs related to AI model performance and drive/pivot strategies based on these KPIs and emerging trends in the industry.
Act as the primary bridge between Clinical SMEs, AI architects, and executive leadership to ensure technical builds align with RCM business goals.
Identify and manage risks unique to AI, such as “data drift,” model hallucinations, and ethical compliance within healthcare regulations.
Manage the roadmap and sprint cycles for the AI development team, balancing long-term research goals with immediate client-specific coding requirements.
Refine the “human-in-the-loop” feedback process to ensure clinical feedback is effectively integrated back into model training.
Define and track key performance indicators (KPIs) such as model accuracy, throughput, and ROI of automated coding vs. manual processes.