Care Innovation - Senior Solution Architect 136-2001

CommunityCare • Tulsa, OK

18d

About The Position

The Senior Solution Architect owns enterprise AI architecture and provides technical leadership across AI initiatives. The role defines target architectures, standards, solution patterns, governance practices, and integration strategies to ensure AI solutions are scalable, secure, compliant, and cost-effective in a HIPAA-regulated healthcare environment. It also provides technical direction and mentorship to Senior Solution Engineers and engineering teams.

Requirements

• Strong understanding of enterprise solution architecture, including AI integration patterns, APIs, data architecture, security, and scalable cloud design within Microsoft Azure or comparable cloud environments.
• Strong understanding of secure SDLC, CI/CD (Azure DevOps or GitHub Actions), secrets management, and operational best practices.
• Strong knowledge of HIPAA and applying security, privacy, and compliance requirements to enterprise systems handling PHI.
• Strong programming and technical foundation, such as Python and/or C#/.NET, sufficient to lead and review engineering work.
• Excellent communication, leadership, and stakeholder engagement skills, including the ability to translate strategy and complex technical concepts for technical and non-technical audiences.
• Ability to work independently while leading multiple initiatives and projects simultaneously.
• Willingness to work in a high-tech, continually evolving, innovative environment.
• Successful completion of a Health Care Sanctions background check.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience.
• At least eight years of progressive experience in solution architecture, enterprise architecture, software, data, or engineering roles, including architecting enterprise-scale systems and delivering AI/ML or generative AI solutions in production environments.
• Experience designing, configuring, and optimizing AI systems, including LLMs, generative AI, AI agents, RAG, prompt engineering, model fine-tuning, evaluation, and AI platform technologies such as Azure OpenAI, Cognitive Services, or comparable LLM/ML platforms with MLOps/LLMOps practices.

Nice To Haves

• Advanced degree.
• Experience working within healthcare, insurance, or public sector environments.
• Experience defining responsible AI or AI governance frameworks in a regulated environment.
• Experience with agent frameworks and orchestration such as LangChain, Semantic Kernel, or Autogen; vector databases; and embeddings.
• Experience with enterprise integration platforms, RPA (UiPath), and Microsoft Power Platform.
• Certifications in Azure Solutions Architect Expert, Azure AI Engineer Associate, or TOGAF.

Responsibilities

• Define and maintain enterprise AI architecture standards, reference architectures, patterns, and blueprints; design scalable, resilient, and secure AI solutions that satisfy functional, performance, cost, and compliance requirements across models, agents, data, applications, and infrastructure.
• Establish architectural standards for retrieval-augmented generation (RAG), agentic systems, data grounding, and human-in-the-loop patterns.
• Review and approve solution designs for alignment with enterprise architecture and long-term technical strategy.
• Lead evaluation, selection, and adoption of AI products, platforms, foundation models, and services, balancing capability, accuracy, performance, cost, security, and compliance.
• Define selection criteria, conduct proofs of concept, and provide build-versus-buy and vendor recommendations to leadership.
• Define enterprise AI guidance, principles, and usage standards, including responsible and ethical AI, safety, privacy, and acceptable-use policies.
• Partner with the Data Governance Board and security teams to align AI practices with governance goals, HIPAA, and regulatory requirements.
• Establish guardrails, evaluation criteria, and risk controls for AI accuracy, bias, safety, and PHI protection.
• Define secure SDLC and engineering standards for AI/ML solutions, including MLOps/LLMOps, CI/CD, versioning, and testing.
• Define enterprise integration architecture standards for APIs, data flows, event messaging, and core system integrations.
• Define operational standards for AI systems, including monitoring, observability, model performance and drift management, incident response, cost management, and continuous improvement.
• Provide technical leadership and mentorship to Senior Solution Engineers and engineering teams through technical direction, best practices, coaching, collaboration, and knowledge sharing.
• Perform other job-related duties as required or assigned.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior