We are seeking a highly skilled Expert level AI/ML Engineer to design, build, and operate autonomous AI/ML systems (Agentic AI) that monitor, decide, and act across cloud and application environments in the US Healthcare domain, specifically supporting Revenue Cycle Management (RCM), clinical, and operational workflows.
This role focuses on developing intelligent agents leveraging:
- GenAI/LLM stacks (Transformers, HuggingFace, LangChain)
- Natural Language Processing (NLP) for clinical and administrative data
- Computer Vision for document and medical imaging analysis
- Predictive analytics & recommender systems
- Multi-agent orchestration
- Big Data pipelines (Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka)
- .NET Core APIs
- RPA platforms (UiPath, Automation Anywhere)
- Cloud-native architectures (AWS, Azure, GCP)
The engineer will deliver predictive, self-healing, and adaptive cloud operations for healthcare organizations, integrating advanced AI/ML frameworks to enable safe, explainable, and autonomous AI systems, while ensuring compliance with HIPAA, GDPR, and SOC 2.
AI/ML Model & Solution Development:
- Design and develop advanced AI/ML models using deep learning, reinforcement learning, supervised, and unsupervised learning techniques to solve business-critical problems in real-time.
- Build cutting-edge solutions across computer vision, NLP, predictive analytics, and recommender systems leveraging frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn.
- Lead the architecture and implementation of AI-powered systems that scale seamlessly, ensuring high performance, security, and reliability across large datasets and cloud environments.
- Integrate AI models into production systems with high efficiency, ensuring they are robust, accurate, and scalable.
- Integrate agents into cloud operations for autonomous monitoring, scaling, and remediation
- Develop deep learning, reinforcement learning, NLP, computer vision, predictive analytics, and recommender systems.
- Optimize AI models using hyperparameter tuning, ensembling, transfer learning, and A/B testing.
- Integrate AI models into production systems ensuring robustness, scalability, and high performance
- Design and develop autonomous AI agents capable of:
- Multi-step decision-making
- Deploy and manage GenAI/LLM models using AWS Bedrock, Azure OpenAI, HuggingFace, LangChain.
- Implement agent memory, context handling, feedback loops, and multi-agent collaboration.
- Integrate agents into healthcare cloud operations for autonomous monitoring, scaling, remediation, and workflow automation, including RCM processes like claims processing, denial management, and patient billing.
- Develop AI/ML models for:
- Natural Language Processing (NLP): text understanding, claims analysis, clinical notes summarization, chatbots for patient interaction
- Computer Vision: document scanning, medical imaging analysis
- Predictive analytics & recommender systems: patient care, claim prediction, revenue forecasting
- Reinforcement learning & deep learning
- Optimize AI models using hyperparameter tuning, model ensembling, transfer learning, and A/B testing.
- Integrate AI models into production systems ensuring robustness, scalability, and high performance, specifically tailored for healthcare and RCM use cases.
Data Engineering & Intelligence Pipelines:
- Build real-time and batch data pipelines using Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka, AWS Glue.
- Preprocess, clean, and transform structured and unstructured healthcare datasets, including patient records, claims data, EHRs, and billing data, for AI/ML model training and inference.
- Enable continuous learning, reinforcement loops, and real-time feedback for AI agents and LLMs.
Cloud, API & AgentOps Integration:
- Expose AI models and agents via secure REST APIs for integration with .NET Core applications and cloud services.
- Implement AgentOps/MLOps practices: monitoring, versioning, rollout, rollback, and auditing of AI agents and models.
- Deploy models and agents on cloud-native platforms (AWS SageMaker, Azure ML, GCP AI Platform) using Docker, Kubernetes, and serverless architectures.
Intelligent Automation & RPA Enablement:
- Integrate AI agents with RPA platforms (UiPath, Automation Anywhere) to automate operational workflows, including claims processing, payment posting, and denial resolution.
- Enable hybrid automation where AI agents coordinate with human approvals as required.
- Orchestrate bots, scripts, and cloud actions using AI-driven orchestration frameworks.
Security, Compliance & Responsible AI:
- Implement guardrails to ensure safe, compliant, and explainable AI behavior.
- Ensure compliance with HIPAA, GDPR, SOC 2, and internal AI governance policies.
- Monitor, audit, and refine agent decisions to prevent model drift, unsafe behavior, or bias, particularly for healthcare data and RCM operations.
Research, Innovation & Leadership:
- Stay updated on AI/ML, GenAI, NLP, multi-agent systems, Agentic AI, and autonomous cloud technologies in healthcare and RCM.
- Contribute to research publications, patents, and internal whitepapers.
- Mentor junior and mid-level engineers on AI/ML best practices, Agentic AI design, and cloud integration.
Documentation & Reporting:
- Maintain detailed documentation for AI/ML models, pipelines, agent workflows, and deployment processes.
- Provide performance reports, insights, and recommendations to stakeholders
- Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
- 4–7+ years of professional experience in AI/ML engineering, autonomous systems, cloud-native AI deployments, and US healthcare/RCM domain.
- Hands-on experience with LLM-based agents, GenAI, NLP, multi-agent orchestration, and AI-driven automation platforms.
- Cloud experience: AWS (Bedrock, SageMaker, Lambda, EC2), Azure (OpenAI, ML), GCP (AI Platform), or hybrid environments.
- Expertise in big data tools: Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka.
- Proficiency in AI/ML frameworks: TensorFlow, PyTorch, Keras, scikit-learn.
- Strong programming skills in Python, R, or Julia, with Git version control.
- Experience with containerization, Kubernetes, Docker, and CI/CD pipelines.
- Familiarity with RPA tools and integration into AI/ML workflows.
- Domain knowledge in US healthcare data standards, RCM workflows, claims processing, and compliance requirements.
Preferred Skills:
- Agentic AI, autonomous systems, and multi-agent orchestration
- Machine learning, deep learning, GenAI, NLP, computer vision, predictive analytics
- LLM stacks: Transformers, HuggingFace pipelines, LangChain, fine-tuning, prompt engineering
- Cloud-native AI deployments (AWS, Azure, GCP)
- Big Data and streaming pipelines
- .NET Core APIs and microservices
- RPA integration and AI-driven automation frameworks
- CI/CD, DevOps, Agile methodologies
- US Healthcare & RCM domain knowledge, HIPAA compliance, clinical and billing data
Soft Skills:
- Strong analytical and problem-solving mindset
- Ability to design safe, explainable, and autonomous AI systems
- Excellent collaboration across cloud, application, and automation teams
- Leadership and mentorship for junior and mid-level engineers
- Strategic thinking to align AI solutions with business goals
- Passion for next-generation AI platforms, autonomous cloud operations, and healthcare transformation
Strategic Impact:
- Enable autonomous cloud operations (Agentic AIOps) in US Healthcare and RCM
- Treat AI agents as first-class platform components
- Seamlessly orchestrate RPA and AI-driven automation for claims, billing, and clinical workflows
- Deploy GenAI/LLM stacks, multi-agent orchestration, NLP, and AI pipelines as intelligence layers.
Kindly Note: At Credence, we uphold the highest standards of integrity in our recruitment process. We do not charge any fees at any stage of the hiring process, and we strictly prohibit any third parties, vendors, or individuals from soliciting money in exchange for job opportunities at Credence.
If you are approached by anyone requesting payment or offering you a position at Credence in exchange for money, do not engage with them. Such actions are fraudulent and not authorized by Credence. Please report any such incidents immediately to our official HR team at [email protected]
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