Care Innovation - Senior Solution Architect 136-2001

Digitailor; Tulsa (OK); On-site; USD 140,000 - 200,000; Full time

Job summary

Digitailor seeks a Senior Solution Architect to own the enterprise AI architecture in a HIPAA-regulated healthcare setting. You’ll define architectures, governance, and integration patterns while mentoring engineering teams and guiding AI initiatives from concept to production.

You will lead the evaluation and adoption of AI platforms, ensure security and regulatory compliance, and drive scalable, cost-conscious solutions across data, models, and applications.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience.
• 8+ years of progressive experience in solution/enterprise architecture, software, data, or engineering roles.
• Experience delivering AI/ML or generative AI in production environments, with HIPAA familiarity.
• Ability to translate strategy and complex concepts for technical and non-technical audiences.

Responsibilities

• Define and maintain enterprise AI architecture standards and patterns across models, data, and integrations.
• Lead evaluation, selection, and adoption of AI platforms and services balancing capability, cost, and compliance.
• Provide technical direction and mentorship to engineers, and drive secure SDLC for AI/ML solutions.
• Partner with governance and security teams to align AI practices with HIPAA and regulatory requirements.

Skills

Enterprise architecture

AI integration

APIs

Data architecture

Security

Cloud design

Education

Bachelor’s degree in CS/IS/Engineering or equivalent

Advanced degree preferred

Tools

Azure DevOps

GitHub Actions

Azure OpenAI

Cognitive Services

Job description

Job Summary

The Senior Solution Architect is responsible for owning the enterprise AI architecture and providing technical leadership across AI initiatives by defining target architectures, standards, solution patterns, governance practices, and integration strategies. This role ensures AI solutions are scalable, secure, compliant, and cost-effective within a HIPAA-regulated healthcare environment while providing technical direction and mentorship to Senior Solution Engineers and engineering teams.

Key Responsibilities

Define and maintain enterprise AI architecture standards, reference architectures, patterns, and blueprints while designing scalable, resilient, and secure AI solutions that meet 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 to ensure alignment to enterprise architecture and long-term technical strategy. Lead the evaluation, selection, and adoption of AI products, platforms, foundation models, and services by balancing technical capabilities, accuracy, performance, cost, security, and compliance requirements. Define selection criteria, conduct proofs of concept, and provide build-vs-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 data protection (PHI). Define the secure SDLC and engineering standards for AI/ML solutions, including MLOps/LLMOps practices, CI/CD, versioning, and testing. Define enterprise integration architecture standards for APIs, data flows, event messaging, and core system integrations to enable scalable, secure, and reliable solutions. 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 by setting technical direction, promoting best practices, and enabling effective delivery through coaching, collaboration, and knowledge sharing. Performs other job-related duties as required or assigned.

Qualifications

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/technical foundation (e.g., Python and/or C#/.NET) sufficient to lead and review engineering work. Excellent communication, leadership, and stakeholder engagement skills, with 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 Health Care Sanctions background check. EDUCATION/EXPERIENCE: Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience; advanced degree preferred. A minimum of eight years in 8+ 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. Prior experience designing, configuring, and optimizing AI systems, including large language models (LLMs), generative AI, AI agents, retrieval-augmented generation (RAG), prompt engineering, model fine-tuning, evaluation, and AI platform technologies such as Azure OpenAI, Cognitive Services, and comparable LLM/ML platforms with MLOps/LLMOps practices. Experience working within healthcare, insurance, or public sector environments preferred. Experience defining responsible AI / AI governance frameworks in a regulated environment preferred. Experience with agent frameworks and orchestration (e.g., LangChain, Semantic Kernel, Autogen), vector databases, and embeddings preferred. Experience with enterprise integration platforms, RPA (UiPath), and Microsoft Power Platform preferred. Certifications in Azure Solutions Architect Expert, Azure AI Engineer Associate, or TOGAF preferred.