Senior AI Engineer – Healthcare RCM & AI Agents
Location: Remote (USA)
Department: Engineering / AI Innovation
Contract Role - US Citizens/GC Holders
About the Client
Our Client is redefining the future of healthcare Revenue Cycle Management (RCM) by applying the latest advancements in artificial intelligence and automation. Leveraging cutting-edge technologies, such as Large Language Models (LLMs), Azure Cognitive Services, and Azure OpenAI, Jorie AI is transforming complex healthcare workflows, enabling smarter claims processing, patient interaction, denial management, and overall operational efficiency. By building intelligent AI agents, we’re improving accuracy, reducing errors, and streamlining processes for healthcare organizations across the United States.
Position Overview
We are seeking a Senior AI Engineer to lead the development and deployment of sophisticated AI agents powered by multiple LLMs for automating and optimizing key RCM processes in the US healthcare ecosystem. This is an opportunity to work with some of the latest and most powerful technologies, including Azure Cognitive Services and Azure OpenAI, to develop scalable AI models that can revolutionize how healthcare providers interact with their patients, process claims, manage payments, and handle denials.
Key Responsibilities
• AI Agent Development: Design, architect, and implement intelligent AI agents that leverage multiple Large Language Models (LLMs) (e.g., GPT, BERT, T5) for automating RCM tasks such as:
- Claims scrubbing and submission
- Denial resolution and prediction
- Eligibility verification and benefits management
- Payment reconciliation and posting
• Azure Cognitive Services & OpenAI Integration: Leverage Azure Cognitive Services (Form Recognizer, Text Analytics, Speech-to-Text, Translator) to build intelligent, scalable document processing workflows and enhance NLP-based communication between healthcare providers and patients. Use Azure OpenAI (GPT, Codex) for advanced decision-making, claims processing, and automated customer support.
• AI Model Deployment & Optimization: Take ownership of deploying large-scale AI models into production, ensuring robust integration with existing RCM workflows. Monitor and optimize models in real-time to maintain high accuracy and ensure seamless performance across production systems.
• ML Systems Design & Infrastructure: Architect and maintain scalable ML systems to process large volumes of healthcare data. Ensure that AI agents are optimized for cloud-native environments using Azure Machine Learning and distributed computing frameworks.
• Data Pipelines & Model Training: Design and build end-to-end data pipelines for handling both structured and unstructured healthcare data (e.g., claims, PDFs, EHR). Develop and fine-tune models using advanced machine learning techniques and transfer learning to handle diverse healthcare scenarios.
• Collaboration Across Teams: Partner with product managers, data scientists, engineers, and healthcare professionals to identify use cases and deliver end-to-end AI-driven solutions. Work closely with the product team to define requirements, prioritize AI features, and integrate them into the broader product roadmap.
• MLOps & Automation: Implement MLOps pipelines to automate model training, deployment, and monitoring. Integrate version control, model retraining, and A/B testing to ensure continuous improvements in model performance.
• Compliance & Security: Ensure all AI models and data systems comply with HIPAA, SOC 2, and other healthcare data security regulations, ensuring that privacy and confidentiality are maintained at all times.
Required Qualifications
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Industry Experience: 5+ years of experience in AI/ML model development, with a focus on large-scale systems, particularly in healthcare or complex enterprise environments.
• AI and ML Expertise: Extensive experience working with Large Language Models (LLMs), particularly for text generation, NLP, and unstructured data analysis. Expertise in developing and deploying AI models using Azure Cognitive Services (Form Recognizer, Text Analytics, Language Understanding, Translator) and Azure OpenAI (GPT, Codex). Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
• Cloud Infrastructure & MLOps: Expertise in deploying and maintaining AI models in cloud environments (preferably Azure), including experience with Azure Machine Learning for model training, deployment, and management. Strong experience with MLOps, version control systems (Git), and CI/CD pipelines for ML model deployment and automation.
Preffered Qualifications:
• Healthcare Data Expertise: Proven experience working with healthcare data standards such as HL7, X12, FHIR, and EDI formats. Knowledge of healthcare workflows, including RCM processes like claims submission, denial management, patient eligibility, and billing.
• Programming Skills: Proficient in Python for machine learning, data analysis, and building scalable AI systems. Experience with API development and integrating AI models into production systems through RESTful APIs.
• Model Deployment & Performance: Strong background in deploying AI solutions in production environments at scale, using distributed computing and cloud platforms to support large, high-traffic systems. Ability to continuously monitor and improve model performance with real-time feedback and automated retraining.
• Experience with Automation Platforms: Familiarity with intelligent automation platforms like Workato, UiPath, or Automation Anywhere for integrating AI into complex workflows.
• AI Research: Contributions to research or development in natural language processing (NLP), computer vision, or deep learning algorithms.
• Industry Recognition: Published papers, open-source contributions, or patents in AI or healthcare technology.
• Regulatory Expertise: In-depth understanding of HIPAA, SOC 2, and other compliance standards related to AI solutions in healthcare.
Location: Remote (USA)
Department: Engineering / AI Innovation
Contract Role - US Citizens/GC Holders
About the Client
Our Client is redefining the future of healthcare Revenue Cycle Management (RCM) by applying the latest advancements in artificial intelligence and automation. Leveraging cutting-edge technologies, such as Large Language Models (LLMs), Azure Cognitive Services, and Azure OpenAI, Jorie AI is transforming complex healthcare workflows, enabling smarter claims processing, patient interaction, denial management, and overall operational efficiency. By building intelligent AI agents, we’re improving accuracy, reducing errors, and streamlining processes for healthcare organizations across the United States.
Position Overview
We are seeking a Senior AI Engineer to lead the development and deployment of sophisticated AI agents powered by multiple LLMs for automating and optimizing key RCM processes in the US healthcare ecosystem. This is an opportunity to work with some of the latest and most powerful technologies, including Azure Cognitive Services and Azure OpenAI, to develop scalable AI models that can revolutionize how healthcare providers interact with their patients, process claims, manage payments, and handle denials.
Key Responsibilities
• AI Agent Development: Design, architect, and implement intelligent AI agents that leverage multiple Large Language Models (LLMs) (e.g., GPT, BERT, T5) for automating RCM tasks such as:
- Claims scrubbing and submission
- Denial resolution and prediction
- Eligibility verification and benefits management
- Payment reconciliation and posting
• Azure Cognitive Services & OpenAI Integration: Leverage Azure Cognitive Services (Form Recognizer, Text Analytics, Speech-to-Text, Translator) to build intelligent, scalable document processing workflows and enhance NLP-based communication between healthcare providers and patients. Use Azure OpenAI (GPT, Codex) for advanced decision-making, claims processing, and automated customer support.
• AI Model Deployment & Optimization: Take ownership of deploying large-scale AI models into production, ensuring robust integration with existing RCM workflows. Monitor and optimize models in real-time to maintain high accuracy and ensure seamless performance across production systems.
• ML Systems Design & Infrastructure: Architect and maintain scalable ML systems to process large volumes of healthcare data. Ensure that AI agents are optimized for cloud-native environments using Azure Machine Learning and distributed computing frameworks.
• Data Pipelines & Model Training: Design and build end-to-end data pipelines for handling both structured and unstructured healthcare data (e.g., claims, PDFs, EHR). Develop and fine-tune models using advanced machine learning techniques and transfer learning to handle diverse healthcare scenarios.
• Collaboration Across Teams: Partner with product managers, data scientists, engineers, and healthcare professionals to identify use cases and deliver end-to-end AI-driven solutions. Work closely with the product team to define requirements, prioritize AI features, and integrate them into the broader product roadmap.
• MLOps & Automation: Implement MLOps pipelines to automate model training, deployment, and monitoring. Integrate version control, model retraining, and A/B testing to ensure continuous improvements in model performance.
• Compliance & Security: Ensure all AI models and data systems comply with HIPAA, SOC 2, and other healthcare data security regulations, ensuring that privacy and confidentiality are maintained at all times.
Required Qualifications
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Industry Experience: 5+ years of experience in AI/ML model development, with a focus on large-scale systems, particularly in healthcare or complex enterprise environments.
• AI and ML Expertise: Extensive experience working with Large Language Models (LLMs), particularly for text generation, NLP, and unstructured data analysis. Expertise in developing and deploying AI models using Azure Cognitive Services (Form Recognizer, Text Analytics, Language Understanding, Translator) and Azure OpenAI (GPT, Codex). Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
• Cloud Infrastructure & MLOps: Expertise in deploying and maintaining AI models in cloud environments (preferably Azure), including experience with Azure Machine Learning for model training, deployment, and management. Strong experience with MLOps, version control systems (Git), and CI/CD pipelines for ML model deployment and automation.
Preffered Qualifications:
• Healthcare Data Expertise: Proven experience working with healthcare data standards such as HL7, X12, FHIR, and EDI formats. Knowledge of healthcare workflows, including RCM processes like claims submission, denial management, patient eligibility, and billing.
• Programming Skills: Proficient in Python for machine learning, data analysis, and building scalable AI systems. Experience with API development and integrating AI models into production systems through RESTful APIs.
• Model Deployment & Performance: Strong background in deploying AI solutions in production environments at scale, using distributed computing and cloud platforms to support large, high-traffic systems. Ability to continuously monitor and improve model performance with real-time feedback and automated retraining.
• Experience with Automation Platforms: Familiarity with intelligent automation platforms like Workato, UiPath, or Automation Anywhere for integrating AI into complex workflows.
• AI Research: Contributions to research or development in natural language processing (NLP), computer vision, or deep learning algorithms.
• Industry Recognition: Published papers, open-source contributions, or patents in AI or healthcare technology.
• Regulatory Expertise: In-depth understanding of HIPAA, SOC 2, and other compliance standards related to AI solutions in healthcare.
