Credence Global Solutions - AI/ML Architect

Description

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 RPAensuring 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.
  
  

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
  
  

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

• Bachelors or Masters 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.
• 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.
  
  

(ref:hirist.tech)