Senior Principal Software Architect

HCA Healthcare, Inc.; Nashville (TN); On-site; USD 180,000 - 260,000; Full time

Job summary

HCA Healthcare, Inc. is seeking a Senior Principal Software Architect to shape enterprise AI strategy across the software engineering lifecycle. You will build and lead a team of AI engineers and architects delivering reusable AI platforms, agentic capabilities, and guardrails to accelerate secure production adoption.

The role spans governance, platform strategy, and hands-on depth to improve engineering productivity, quality, security, and developer experience at scale.

Qualifications

• Bachelor’s degree in computer science, engineering, or a related field is required.
• 12–15+ years of progressive experience in software engineering, enterprise architecture, platform engineering, cloud, data, or AI/ML with leadership responsibility.
• Demonstrated success architecting and influencing enterprise-scale platforms with long operational lifespans.
• Proven ability to affect technical direction across multiple teams, portfolios, or lines of business.
• Proven success defining and delivering enterprise-scale architecture or platform strategies across multiple business or technology organizations.
• Experience moving generative AI, machine learning, or agentic capabilities from experimentation into production-scale adoption.
• Minimum 5 years of experience managing engineering organizations and technical teams.

Responsibilities

• Define and own the enterprise architecture and multi-year technical strategy for AI enablement across the software engineering lifecycle.
• Establish reference architectures, reusable patterns, standards, and decision frameworks for generative AI, LLMs, retrieval-augmented generation, agentic systems, evaluation, observability, and AI lifecycle management.
• Serve as a senior technical authority for high-impact architecture decisions spanning multiple products, platforms, and engineering organizations.
• Continuously assess technology shifts and evolve the architecture to reduce fragmentation, manage technical debt, and preserve enterprise flexibility.
• Lead the strategy and delivery of AI capabilities that improve requirements, architecture, coding, testing, security, DevSecOps, reliability, modernization, and knowledge workflows.
• Create scalable adoption models, paved roads, reusable agents, and platform capabilities that help engineering teams move to secure production use.
• Partner with software engineering leaders to redesign workflows around human and AI collaboration with clear accountability, review, and control points.
• Define outcome measures and feedback loops for adoption, engineering velocity, quality, security, and business value.

Skills

Generative AI architectures

LLMs

RAG

Agentic AI systems

Cloud-native AI platforms

Leadership of engineering orgs

Executive communication

Education

Bachelor’s degree in Computer Science or related field

12–15+ years of software/enterprise architecture leadership

Tools

Vertex AI (GCP)

Gemini

Kubernetes

Docker

Job description

This position is incentive eligible.

Last year our HCA Healthcare colleagues invested over 156,000 hours volunteering in our communities. As a Senior Principal Sware Architect with HCA Healthcare you can be a part of an organization that is devoted to giving back!

Job Summary and Qualifications

Position Summary

The Senior Principal Software Architect is a Director-level technology leader responsible for shaping and executing the enterprise architecture, engineering strategy, and adoption model for Artificial Intelligence across Software Engineering. This leader will build and lead a team of AI engineers and architects who create reusable AI platforms, agentic engineering capabilities, reference architecture, and guardrails that enable software teams to design, build, test, secure, deploy, and operate solutions more effectively.

Operating across organizational boundaries, this role translates enterprise priorities into a multi-year AI enablement roadmap, establishes technical standards and governance, and partners with senior technology leaders to scale trusted AI adoption. The position combines people leadership, enterprise architecture, platform strategy, and selective hands-on technical depth, with accountability for measurable improvements in engineering productivity, quality, security, reliability, and developer experience.

Key Responsibilities

Enterprise Architecture & Technical Authority

• Define and own the enterprise architecture and multi-year technical strategy for AI enablement across the software engineering lifecycle.
• Establish reference architectures, reusable patterns, standards, and decision frameworks for generative AI, large language models, retrieval-augmented generation, agentic systems, evaluation, observability, and AI lifecycle management.
• Serve as a senior technical authority for high-impact architecture decisions spanning multiple products, platforms, and engineering organizations.
• Continuously assess technology shifts and evolve the architecture to reduce fragmentation, manage technical debt, and preserve enterprise flexibility.

AI-Enabled Software Engineering Transformation

• Lead the strategy and delivery of AI capabilities that improve requirements, architecture, coding, testing, security, DevSecOps, reliability, modernization, and engineering knowledge workflows.
• Create scalable adoption models, paved roads, reusable agents, and platform capabilities that help engineering teams move from experimentation to secure production use.
• Partner with software engineering leaders to redesign workflows around human and AI collaboration while maintaining clear accountability, review, and control points.
• Define outcome measures and feedback loops for adoption, engineering velocity, quality, security, developer experience, reuse, and business value.

People Leadership and Organizational Capability

• Build, lead, coach, and develop a high-performing team of AI engineers and architects; establish clear goals, operating mechanisms, and accountability for outcomes.
• Create a talent strategy that attracts, develops, and retains advanced AI engineering and architecture capabilities.
• Provide technical and career leadership through architecture reviews, mentoring, succession planning, and development opportunities.
• Foster an inclusive culture of innovation, disciplined experimentation, continuous learning, engineering excellence, and responsible decision-making.

AI Platforms, Agents, and Engineering Foundations

• Guide the architecture and evolution of enterprise-scale AI platforms and agentic systems that integrate safely with developer tools, enterprise systems, data, and software delivery workflows.
• Establish patterns for model selection, grounding, orchestration, context management, tool integration, prompt and agent lifecycle management, evaluation, telemetry, and resilience.
• Ensure platform capabilities are reusable, interoperable, cost-conscious, observable, supportable, and designed for enterprise-scale adoption.
• Remain selectively hands-on in complex areas to validate technical direction, unblock teams, and de-risk critical initiatives.

Governance, Security, and Responsible AI

• Embed security, privacy, compliance, responsible AI, data protection, human oversight, and auditability into architecture and engineering practices.
• Define technical controls for model and tool access, sensitive data handling, identity, secrets, content safety, evaluation, traceability, and third-party integration.
• Partner with Security, Risk, Privacy, Legal, Data, and Architecture stakeholders to establish practical governance that enables innovation while protecting the enterprise.
• Ensure solutions comply with enterprise architecture standards, approved technology patterns, and applicable organizational policies.

Executive Partnership and Enterprise Influence

• Advise senior leaders on AI opportunities, architectural tradeoffs, investment priorities, delivery risks, build-versus-buy decisions, and organizational readiness.
• Translate complex AI and software engineering concepts into clear recommendations, roadmaps, and value narratives for executive and business audiences.
• Align cross-functional leaders around shared platforms, standards, priorities, and adoption plans; resolve competing technical directions and dependencies.
• Represent the organization’s AI-enabled software engineering vision in internal forums and with strategic technology partners when appropriate.

Portfolio Execution and Value Realization

• Own an integrated portfolio of AI engineering initiatives from strategy and incubation through adoption and operational maturity.
• Establish planning, prioritization, architecture review, risk management, financial stewardship, and delivery mechanisms for the team.
• Use measurable outcomes to guide investment decisions, discontinue low-value approaches, and scale capabilities that demonstrate enterprise impact.
• Practice and adhere to the organization’s Code of Conduct, Mission, and Values, and perform other duties as assigned.

Education & Experience

• Bachelor’s degree in computer science, Engineering, or a related field, or equivalent practical experience. Required
• 12–15+ years of progressive experience in software engineering, enterprise architecture, platform engineering, cloud, data, or AI/ML, including significant leadership responsibility. Required
• Demonstrated success architecting and influencing enterprise-scale platforms with long operational lifespans. Required
• Proven ability to affect technical direction across multiple teams, portfolios, or lines of business. Required
• Proven success defining and delivering enterprise-scale architecture or platform strategies across multiple business or technology organizations. Required
• Demonstrated experience moving generative AI, machine learning, or agentic capabilities from experimentation into governed, production-scale adoption. Required
• Minimum 5 years of experience managing engineering organizations and technical teams. Required

Licenses, Certifications, & Training

• Advanced cloud or AI certifications (e.g., GCP Professional ML Engineer, Cloud Architect) preferred.

Knowledge, Skills, Abilities, Behaviors

• Deep mastery of Generative AI architectures, including LLMs, RAG, agentic systems, and vector search. Required
• Expert-level understanding of cloud-native AI platforms, especially Vertex AI and large-scale distributed systems. Required
• Exceptional systems thinking with a focus on leverage, longevity, and platform impact. Required
• Ability to operate independently with minimal guidance while aligning tightly with executive intent. Required
• Influence through credibility, clarity, and technical excellence rather than formal authority. Required
• Demonstrated growth mindset with industry leading curiosity, continuously identifying, evaluating, and defining adoption strategies for emerging AI technologies, frameworks, and paradigms with multiyear enterprise impact. Required
• Ability to frame entirely new technical problem spaces, set precedent where none exists, and establish clarity for the organization in environments of extreme ambiguity Required
• Operates as a trusted technical authority whose judgment materially influences executive technology decisions. Required
• Recognized enterprise expert in GCP Vertex AI and Generative AI platforms, including Gemini, Vertex AI Studio, and foundational-model orchestration; establishes reference architectures and standards consumed across multiple teams. Required
• Domain-defining expertise in Retrieval Augmented Generation (RAG) architectures, including evaluation methodologies, governance models, cost optimization, scalability, security, and regulatory alignment. Required
• Owns the enterprise strategy for Vector Stores, embedding models, indexing patterns, and lifecycle management; sets long term direction and deprecates antipatterns. Preferred
• Authoritative expertise in grounding strategies, including data contracts, schema evolution, traceability, reliability guarantees, and factuality enforcement at scale. Preferred
• Enterprise level ownership of MLOps strategy, including platform governance, CI/CD at scale, observability, lineage, drift detection, and reliability engineering across AI systems. Required
• Mastery of cloud native architecture for AI platforms spanning serverless, containerized, hybrid, and multi cloud solutions; anticipates operational and financial tradeoffs years in advance. Required
• Defines and reviews architecture artifacts (e.g., Visio, reference diagrams, platform blueprints) used to align engineering, security, and executive stakeholders. Required
• Strategic leadership in interoperability frameworks, including the Model Context Protocol; defines adoption models, extension points, and governance for cross platform LLM integration. Required
• Expert-level understanding of Agentic AI systems, including autonomous, multi agent, and tool driven architectures; evaluates readiness and risk for production grade deployment. Required
• Architectural level fluency across enterprise stacks, with responsibility for defining integration patterns across: SQL and NoSQL databases (e.g., SQL Server, CosmosDB, MongoDB) Large-scale ETL and data platforms (e.g., GCP DataFlow, Azure Data Factory) Event-driven and streaming systems (e.g., Azure EventHub, GCP Pub/Sub, Kafka) Distributed microservice environments (Docker/Kubernetes, Java, Python, NodeJS, C#) Required
• Defines enterprise integration strategies for AI systems within CRM, ERP, eCommerce, and EMR/EHR platforms, including security boundaries, compliance, and data stewardship. Preferred
• Authoritative understanding of Agile and modern SDLC practices, with a track record of influencing delivery models at organizational scale without direct authority. Required
• Exceptional executive level communication ability, capable of translating deeply technical concepts into strategic options, risks, and tradeoffs for senior leadership. Required
• Serves as a technical steward for the engineering community, leading architecture councils, internal AI forums, and cross organizational technical initiatives. Required
• Elite problem-solving capabilities