What are the responsibilities and job description for the Principal AI Solutions Architect position at Snowrelic Inc?
Job Description:
Job Title: Principal AI Solutions Architect Cloud & Enterprise AI
Location: 95% Remote Occasional Onsite Meetings in Washington, D.C.
Position Overview
Patient-Centered Outcomes Research Institute is seeking a highly experienced Principal AI Solutions Architect to lead the design and implementation of secure, scalable, and enterprise-grade AI and cloud solutions supporting healthcare research, data modernization, and digital transformation initiatives.
This role requires deep expertise in AWS cloud architecture, Generative AI ecosystems, enterprise systems integration, MLOps, AI governance, and cloud-native application design. The ideal candidate will serve as a strategic technical leader responsible for architecting AI-enabled platforms while ensuring security, compliance, governance, and operational excellence.
The architect will collaborate closely with enterprise leadership, data engineering teams, cybersecurity, DevSecOps, and application development teams to drive innovation using modern AI technologies including LLMs, RAG architectures, MCP (Model Context Protocol), AI Guardrails, and enterprise AI orchestration frameworks.
Key Responsibilities
Design and implement enterprise-scale AWS cloud architectures supporting AI/ML and GenAI workloads.
Architect Scalable, Resilient, And Secure Cloud-native Solutions Using:
Microservices
Containers
APIs
Event-driven architectures
Serverless computing
Lead Architecture And Integration Efforts For:
Generative AI platforms
LLM orchestration
AI agents
RAG pipelines
Vector databases
Knowledge graph integrations
Design And Implement AI Governance Frameworks Including:
Responsible AI
AI Guardrails
Model monitoring
Risk management
Compliance controls
Implement and support MCP (Model Context Protocol) integrations and AI interoperability solutions.
Architect Enterprise MLOps Pipelines For:
Model training
Validation
Deployment
Observability
Lifecycle management
Design Secure AI Solutions Leveraging AWS Services Such As:
SageMaker
Bedrock
Lambda
ECS/EKS
API Gateway
DynamoDB
S3
IAM
CloudWatch
Collaborate With DevSecOps Teams To Implement:
CI/CD pipelines
Infrastructure as Code (Terraform/CloudFormation)
Security automation
Compliance scanning
Develop architecture standards, governance models, and technical roadmaps.
Provide technical leadership, mentoring, and architectural guidance across engineering teams.
Evaluate emerging AI technologies and recommend enterprise adoption strategies.
Support enterprise modernization initiatives aligned with healthcare and research data platforms.
Required Qualifications
10 years of experience in:
Enterprise Architecture
Cloud Engineering
Systems Architecture
Solution Architecture
5 years of hands-on AWS cloud architecture experience.
Strong Expertise In:
AI/ML architecture
Generative AI
Large Language Models (LLMs)
AI orchestration frameworks
MLOps
Experience With:
MCP (Model Context Protocol)
AI Guardrails
Responsible AI frameworks
Strong Experience Designing:
Distributed systems
Enterprise integrations
Cloud-native platforms
Expertise In:
API architecture
Event-driven systems
Kubernetes
Docker
Serverless computing
Experience With Infrastructure As Code:
Terraform
CloudFormation
Strong DevSecOps and CI/CD implementation experience.
Strong Understanding Of:
Security architecture
Governance
Compliance
Enterprise cloud controls
Strong communication and stakeholder management skills.
Preferred Qualifications:
AWS Certified Solutions Architect Professional
AWS AI/ML Specialty Certification
Experience With:
LangChain
OpenAI APIs
Bedrock Agents
Vector databases
Knowledge Graphs
RAG architectures
Experience Supporting Healthcare, Research, Nonprofit, Or Federal Organizations Preferred.
Experience implementing enterprise AI governance and Responsible AI initiatives.
Familiarity with healthcare data modernization and interoperability standards is a plus.
Technical Skills:
AWS Cloud Architecture
Generative AI / GenAI
LLM Integration
MCP (Model Context Protocol)
AI Guardrails
Responsible AI
MLOps
Terraform / CloudFormation
Kubernetes / Docker
Python
API Design
DevSecOps
CI/CD Pipelines
Enterprise Systems Integration
Data & AI Governance
RAG Architectures
Vector Databases
Bedrock / SageMaker
Work Environment
95% Remote
Occasional onsite meetings in Washington, D.C.
Collaborative enterprise architecture and innovation environment
Opportunity to lead next-generation AI transformation initiatives
Job Title: Principal AI Solutions Architect Cloud & Enterprise AI
Location: 95% Remote Occasional Onsite Meetings in Washington, D.C.
Position Overview
Patient-Centered Outcomes Research Institute is seeking a highly experienced Principal AI Solutions Architect to lead the design and implementation of secure, scalable, and enterprise-grade AI and cloud solutions supporting healthcare research, data modernization, and digital transformation initiatives.
This role requires deep expertise in AWS cloud architecture, Generative AI ecosystems, enterprise systems integration, MLOps, AI governance, and cloud-native application design. The ideal candidate will serve as a strategic technical leader responsible for architecting AI-enabled platforms while ensuring security, compliance, governance, and operational excellence.
The architect will collaborate closely with enterprise leadership, data engineering teams, cybersecurity, DevSecOps, and application development teams to drive innovation using modern AI technologies including LLMs, RAG architectures, MCP (Model Context Protocol), AI Guardrails, and enterprise AI orchestration frameworks.
Key Responsibilities
Design and implement enterprise-scale AWS cloud architectures supporting AI/ML and GenAI workloads.
Architect Scalable, Resilient, And Secure Cloud-native Solutions Using:
Microservices
Containers
APIs
Event-driven architectures
Serverless computing
Lead Architecture And Integration Efforts For:
Generative AI platforms
LLM orchestration
AI agents
RAG pipelines
Vector databases
Knowledge graph integrations
Design And Implement AI Governance Frameworks Including:
Responsible AI
AI Guardrails
Model monitoring
Risk management
Compliance controls
Implement and support MCP (Model Context Protocol) integrations and AI interoperability solutions.
Architect Enterprise MLOps Pipelines For:
Model training
Validation
Deployment
Observability
Lifecycle management
Design Secure AI Solutions Leveraging AWS Services Such As:
SageMaker
Bedrock
Lambda
ECS/EKS
API Gateway
DynamoDB
S3
IAM
CloudWatch
Collaborate With DevSecOps Teams To Implement:
CI/CD pipelines
Infrastructure as Code (Terraform/CloudFormation)
Security automation
Compliance scanning
Develop architecture standards, governance models, and technical roadmaps.
Provide technical leadership, mentoring, and architectural guidance across engineering teams.
Evaluate emerging AI technologies and recommend enterprise adoption strategies.
Support enterprise modernization initiatives aligned with healthcare and research data platforms.
Required Qualifications
10 years of experience in:
Enterprise Architecture
Cloud Engineering
Systems Architecture
Solution Architecture
5 years of hands-on AWS cloud architecture experience.
Strong Expertise In:
AI/ML architecture
Generative AI
Large Language Models (LLMs)
AI orchestration frameworks
MLOps
Experience With:
MCP (Model Context Protocol)
AI Guardrails
Responsible AI frameworks
Strong Experience Designing:
Distributed systems
Enterprise integrations
Cloud-native platforms
Expertise In:
API architecture
Event-driven systems
Kubernetes
Docker
Serverless computing
Experience With Infrastructure As Code:
Terraform
CloudFormation
Strong DevSecOps and CI/CD implementation experience.
Strong Understanding Of:
Security architecture
Governance
Compliance
Enterprise cloud controls
Strong communication and stakeholder management skills.
Preferred Qualifications:
AWS Certified Solutions Architect Professional
AWS AI/ML Specialty Certification
Experience With:
LangChain
OpenAI APIs
Bedrock Agents
Vector databases
Knowledge Graphs
RAG architectures
Experience Supporting Healthcare, Research, Nonprofit, Or Federal Organizations Preferred.
Experience implementing enterprise AI governance and Responsible AI initiatives.
Familiarity with healthcare data modernization and interoperability standards is a plus.
Technical Skills:
AWS Cloud Architecture
Generative AI / GenAI
LLM Integration
MCP (Model Context Protocol)
AI Guardrails
Responsible AI
MLOps
Terraform / CloudFormation
Kubernetes / Docker
Python
API Design
DevSecOps
CI/CD Pipelines
Enterprise Systems Integration
Data & AI Governance
RAG Architectures
Vector Databases
Bedrock / SageMaker
Work Environment
95% Remote
Occasional onsite meetings in Washington, D.C.
Collaborative enterprise architecture and innovation environment
Opportunity to lead next-generation AI transformation initiatives