Position Overview
We are seeking a highly experienced AI/ML Architect to define, design, and govern enterprise-scale
AI/ML and Agentic AI platforms. This role is responsible for architecting GenAI/LLM-powered,
autonomous, and cloud-native AI systems that operate across healthcare and Revenue Cycle
Management (RCM) workflows.
The AI/ML Architect will provide technical leadership and architectural direction across intelligent
agents, multi-agent orchestration, NLP, predictive analytics, Big Data platforms, cloud infrastructure,
APIs, and RPA—ensuring solutions are scalable, secure, compliant, explainable, and production-ready.
This is a hands-on architecture and strategy role, bridging business outcomes, engineering execution, and responsible
AI governance.
Job Roles & Responsibilities
AI/ML & Agentic AI Architecture
Define end-to-end AI/ML and Agentic AI architecture for enterprise platforms.
Architect Autonomous AI Systems Capable Of
- Goal-based reasoning
- Multi-step decision-making
- Tool/API orchestration
- Multi-agent collaboration
Design GenAI/LLM architectures using AWS Bedrock, Azure OpenAI, HuggingFace, LangChain,
and Transformer-based models.
Establish Architectural Patterns For
- Agent memory, context management, feedback loops
- Human-in-the-loop decision governance
Safe autonomous execution AI-Driven Cloud Enablement
Architect Solutions Leveraging AWS Bedrock For GenAI-powered
- Infrastructure optimization
- Predictive scaling
- Log intelligence and anomaly detection
Enable seamless integration of AI/ML models into application and infrastructure layers
via APIs
Credence Resource Management v2.1 Effective – January 20, 2026
GenAI, NLP & Advanced AI Capabilities
Architect AI Solutions Across
- Natural Language Processing (NLP) – clinical notes, claims text, coding,
summarization, chatbots
- Computer Vision – document ingestion, imaging, OCR
- Predictive analytics & recommender systems – revenue forecasting, denial
prediction, patient engagement
- Deep learning & reinforcement learning
Define standards for prompt engineering, fine-tuning, RAG (Retrieval-Augmented
Generation), and LLM lifecycle management.
Data, Big Data & Intelligence Platforms
Architect Enterprise Data And AI Intelligence Platforms Using
- Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka
Design real-time and batch pipelines feeding AI agents with:
- Logs, metrics, events
- Structured & unstructured healthcare and RCM data
Enable continuous learning pipelines and reinforcement loops for AI agents and models.
Cloud-Native & Platform Architecture
Define Cloud-native AI Architectures Across
- AWS (Bedrock, SageMaker, Lambda, EC2, EKS)
- Azure (OpenAI, Azure ML)
- GCP (AI Platform)
Design microservices and API-first architectures, leveraging .NET Core APIs as AI/agent
control planes.
Establish Deployment Standards Using
- Docker, Kubernetes
- Serverless architectures
CI/CD and DevOps pipelines
AgentOps, MLOps & Platform Governance
Define AgentOps / MLOps Frameworks Covering
- Model, agent, prompt, and tool versioning
- Monitoring, observability, and drift detection
- Safe rollout, rollback, and experimentation strategies
Architect auditability and explainability into AI and agent workflows.
Ensure AI systems meet enterprise reliability, scalability, and resilience standards
Automation, RPA & Orchestration
Architect integration between AI agents and RPA platforms (UiPath, Automation Anywhere).
Enable AI-driven Orchestration Of
- Bots
- Scripts
- Cloud operations
Support hybrid automation where AI agents coordinate with human approvals
Credence Resource Management v2.1 Effective – January 20, 2026
Security, Compliance & Responsible AI
Define AI Governance And Security Architecture, Ensuring
- HIPAA, GDPR, SOC 2 compliance
- Secure model access, data isolation, and role-based controls
Establish Guardrails For
- Ethical AI
- Bias mitigation
- Explainable and auditable decision-making
Oversee secure deployment of AI models and agents in regulated healthcare environments.
US Healthcare & RCM Domain Enablement
Architect AI Solutions Supporting
- Claims processing
- Coding & billing automation
- Denial prediction and management
- Payment posting and revenue forecasting
Ensure architectures align with US healthcare data standards, workflows, and compliance
Requirements
Leadership & Strategic Influence
Act as the AI/ML architectural authority, guiding engineers, data scientists, and platform
teams.
Partner with product, cloud, security, and business leaders to align AI strategy with business
outcomes.
Mentor senior engineers and contribute to architecture reviews, reference designs, and best
practices.
Drive innovation through research, POCs, whitepapers, and AI thought leadership
Candidate Requirements
Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
8–12+ years of experience in AI/ML engineering, data platforms, and cloud architecture.
4+ years in AI/ML architecture or technical leadership roles.
Proven experience designing GenAI, NLP, LLM-based, and Agentic AI systems.
Strong background in US Healthcare and RCM platforms.
Hands-on experience with multi-agent systems, autonomous AI, and AI-driven automation.
Technical Expertise
Agentic AI, autonomous systems, multi-agent orchestration
GenAI & LLM stacks: Transformers, HuggingFace, LangChain, RAG, fine-tuning, prompt
engineering
AI/ML frameworks: TensorFlow, PyTorch, Keras, scikit-learn
Big Data & Streaming: Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka
Cloud platforms: AWS, Azure, GCP (AI/ML services)
APIs & microservices: .NET Core, REST, event-driven architectures
RPA & automation platforms
DevOps, CI/CD, Kubernetes, Docker
Credence Resource Management v2.1 Effective – January 20, 2026
AI governance, security, and compliance frameworks.
Skillset
Strong architectural and systems-thinking mindset
Ability to translate complex AI concepts into business-aligned solutions
Executive-level communication and stakeholder engagement
Leadership, mentorship, and influence across large teams
Passion for autonomous AI platforms and healthcare transformation
Strategic Impact
Establish enterprise AI/ML and Agentic AI platforms
Enable autonomous, self-healing, and intelligent cloud operations
Position AI agents as first-class platform components
Drive scalable, compliant, and responsible GenAI adoption in healthcare & RCM
Skills: ml,agenticai,ai,rcm,llm,revenuecyclemanagement,genai,us healthcare
