Principal AI Solutions Architect - Homebased

NOTE: This role REQUIRES Experience with Model Context Protocol (MCP) Server, DevOps deployment, Cloud Architecture, and Enterprise AI Technologies.

As leaders in the pharmaceutical support industry, UBC is devoted to empowering health solutions for a better tomorrow. We take pride in improving patient outcomes and advancing healthcare. At UBC, we provide services to enhance the entire drug development process and commercialization lifecycle - From clinical trial support to real-world evidence generation. 

Embark on a rewarding career journey with UBC! Grow your career while making a meaningful impact on the world around you. UBC fosters a culture built on our Core Values of Respect, Accountability, Innovation, Quality, Integrity, and Collaboration. We believe in an inclusive workplace that fosters creativity.

If you are seeking a career that will challenge, inspire, and reward you, join us at UBC! 

Brief Description:

Join UBC’s AI Center of Excellence as the Principal AI Solutions Architect, where you will define the technical architecture, engineering patterns, and implementation approach for reusable AI capabilities across the enterprise. You will establish how AI capabilities are designed, built, deployed, integrated, monitored, secured, and reused across business systems while partnering with the application, platform, infrastructure, data, DevOps, and security teams to ensure reusable AI capabilities align with enterprise architecture, operational standards, and business needs. 

As the senior technical leader for AI solution architecture, you will define how reusable capabilities are integrated, deployed, scaled, monitored, secured, and supported within UBC’s enterprise technology environment. This is a hands-on architecture role that combines deployment and platform design, implementation guidance, technical review, and direct contribution to solution design and delivery.

Specific Job Duties:

• Technical Leadership: Serve as the principal technical authority for AI solution and deployment architecture and provide architectural guidance across the Enterprise AI Center of Excellence and delivery teams. 
• AI Capability Architecture: Define the technical structure, interfaces, dependencies, controls, and operational requirements needed for reusable AI capabilities to be integrated and deployed consistently. 
• AI Solution and Integration Design: Design end-to-end AI solutions and work with application, platform, infrastructure, data, and engineering teams to integrate AI capabilities into enterprise applications and workflows.
• Architecture Standards and Reuse: Establish reference architectures, engineering patterns, and technical standards that promote security by design, consistency, reuse, maintainability, and alignment with enterprise architecture. 
• Hands-on Solution Development: Contribute directly to proof-of-concept and production-oriented AI solutions, deployment patterns, and technical components that accelerate delivery and establish sound engineering practices.
• Technical Review and Enablement: Review solution designs and implementations, provide technical guidance, and work with application, platform, DevOps, security, and delivery teams to enable effective deployment and operation of AI capabilities.
• Deployment and Lifecycle Architecture: Define how AI capabilities are deployed, configured, scaled, versioned, monitored, released, rolled back, supported, and managed across their lifecycle in partnership with platform, DevOps, security, and operational teams.
• Technology and Roadmap Direction: Evaluate emerging technologies and provide technical input into capability sourcing, adoption decisions, and the enterprise AI architecture and capability roadmap.

Supervisory Responsibility:

N/A

Desired Skills and Qualifications:

Education:

• Bachelor’s degree in computer science, software engineering, computer engineering, information systems, or a related technical discipline, or equivalent practical experience. 
• Master’s degree preferred.

Experience:

• 10+ years of experience in software engineering, solution architecture, platform engineering, Dev Ops, or related technical roles. 
• 4+ years of experience designing and implementing AI, machine learning, cloud-native, data-intensive, or distributed enterprise solutions. 
• Significant experience designing, deploying, and supporting production applications, services, or platforms.
• Experience defining architectural standards, deployment strategies, and engineering practices across multiple delivery teams. 
• Experience providing technical leadership and architectural guidance without direct supervisory responsibility. 

Technical Expertise:

• AI Solution Architecture: Deep understanding of modern AI systems, including large language models, retrieval-augmented generation, AI agents, orchestration, tool integration, structured outputs, human review, and workflow integration. 
• Software & Platform Engineering: Strong software engineering background with experience designing cloud-native architectures, APIs, containers, CI/CD, deployment automation, DevOps, and production operations.
• Enterprise Integration: Experience integrating reusable capabilities with enterprise applications, workflow platforms, identity services, APIs, messaging systems, and data platforms. 
• Security by Design: Strong understanding of secure architecture principles, identity and access management, data protection, observability, audit logging, resiliency, and operational controls.
• Architecture Leadership: Ability to establish technical direction, reusable engineering patterns, architecture standards, and deployment approaches that support scalable enterprise AI.
• Data Architecture: Knowledge of structured and unstructured data, retrieval patterns, vector search, semantic retrieval, data movement, lineage, and access controls.

Preferred Qualifications:

• Experience with Azure AI, Azure OpenAI, Snowflake Cortex AI, LangGraph, Model Context Protocol (MCP), or comparable enterprise AI technologies. 
• Experience using Claude Code, GitHub Copilot, Cursor, or similar agentic development tools. 
• Experience with workflow orchestration or business-process automation platforms such as Camunda or similar technologies.
• Experience working in healthcare, life sciences, clinical research, pharmacovigilance, patient access, or another regulated industry.
• Familiarity with GxP, 21 CFR Part 11, HIPAA, GDPR, software validation, or other regulated-system expectations. 

Additional Skills:

• Strategic Thinking: Ability to connect technical decisions to enterprise architecture, long-term reuse, scalability, and business strategy. 
• Problem Solving: Advanced ability to resolve complex architecture and engineering challenges through practical and maintainable solutions. 
• Communication: Ability to communicate architectural concepts, tradeoffs, and recommendations clearly to executives, engineers, scientists, architects, and business stakeholders. 
• Collaboration: Ability to work effectively across Product Management, AI Science, Application Development, Platform Engineering, DevOps, Security, Data, Quality, and business teams. 
• Adaptability: Ability to evaluate emerging technologies and evolve architectural direction while maintaining engineering discipline and operational reliability.

DEI Statement

UBC is proud to be an equal opportunity employer and does not discriminate because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state, or local protected class. We are committed to a diverse, equitable, and inclusive culture that fosters respect for each other, our clients, and our patients.

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