Principal AI Applied Engineer

About this role

Become a part of our caring community   Every large organization is making critical decisions today about how it will leverage AI over the next decade. Few have leaders who can both define that vision and demonstrate its viability through hands-on engineering. This role requires both.

We build the platform that transforms millions of clinical documents into trusted, actionable data. Our systems use large language models (LLMs) to read medical records, extract structured facts, answer complex questions with citations to source documents, and route difficult cases to human experts. These capabilities support decisions that impact real healthcare outcomes for members.

As a Principal AI Applied Engineer, you will define the technical strategy, architectural standards, and long-term vision for AI-enabled products across the organization. You will influence enterprise-wide decisions regarding AI platforms, model strategies, engineering standards, and technology investments while remaining deeply hands-on in prototyping, experimentation, architecture, and software development.

This is the highest-level individual contributor role within the AI Applied Engineering organization. Success requires exceptional technical depth, organizational influence, strategic thinking, and the ability to translate emerging AI capabilities into scalable, reliable, and responsible production systems.

Why Join Us • Shape the long-term AI architecture and engineering direction for a large enterprise healthcare organization.

• Influence how AI-enabled products are designed, built, evaluated, deployed, and governed across multiple teams.

• Drive strategic decisions involving models, vendors, platforms, infrastructure, and shared capabilities.

• Prototype and validate emerging technologies before the organization invests at scale.

• Establish engineering standards and platform capabilities that will influence the business for years to come.

• Solve complex challenges involving trust, explainability, compliance, reliability, and operational excellence in AI systems.

• Work on systems that process healthcare information at significant scale and directly support member outcomes.

• Remain hands-on with architecture, software development, experimentation, and technical problem solving.

Key Responsibilities • Define and drive the long-term technical strategy, reference architecture, and technology roadmap for AI-enabled products and platforms.

• Establish architectural patterns and best practices for retrieval systems, LLM orchestration, agentic workflows, human-in-the-loop processes, evaluation frameworks, and AI governance.

• Lead organization-wide technical decision-making regarding model strategy, vendor selection, platform investments, build-versus-buy decisions, and shared infrastructure capabilities.

• Design and champion scalable architectures that balance reliability, performance, security, compliance, maintainability, and cost efficiency.

• Establish engineering standards for evaluation, observability, model lifecycle management, responsible AI practices, and operational excellence.

• Build prototypes, proofs of concept, and reference implementations that de-risk emerging technologies and strategic platform investments.

• Lead cross-functional architecture reviews and provide technical guidance across multiple engineering teams.

• Mentor Lead Engineers, Senior Engineers, and emerging technical leaders through architecture reviews, design feedback, coaching, and technical sponsorship.

• Develop and communicate technical roadmaps aligned with business strategy and organizational objectives.

• Partner with executive leadership to translate business priorities into technical strategies and communicate technical opportunities, risks, and tradeoffs.

• Represent engineering in strategic vendor evaluations, compliance discussions, governance reviews, and enterprise planning initiatives.

• Drive adoption of common platforms, standards, and engineering practices across teams.

• Ensure AI systems are designed and operated in accordance with privacy, security, compliance, governance, and auditability requirements.

Use your skills to make an impact   Required Qualifications • Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

• 12+ years of software engineering experience, including extensive experience designing and operating large-scale distributed systems.

• Proven track record of designing, delivering, and operating production AI-enabled platforms or products at enterprise scale.

• Deep expertise in large language model technologies, AI platform design, retrieval-augmented generation (RAG), agentic systems, orchestration frameworks, evaluation methodologies, and AI operations.

• Extensive experience designing cloud-native architectures and highly scalable, resilient systems.

• Demonstrated ability to define technical direction that influenced teams, organizations, or product portfolios beyond direct reporting relationships.

• Experience establishing engineering standards, architectural frameworks, or technical platforms that have been successfully adopted across multiple teams.

• Strong understanding of AI operational challenges, including model reliability, latency, cost optimization, governance, observability, and risk management.

• Demonstrated success influencing executive stakeholders and driving organizational decisions through technical leadership and evidence-based recommendations.

• Expert-level proficiency in Python and/or TypeScript/JavaScript.

• Ability to prototype, debug, review, and contribute code across multiple layers of the technology stack.

Preferred Qualifications • Experience leading AI platform strategy across multiple business units or product organizations.

• Experience developing enterprise standards for AI evaluation, testing, observability, governance, and responsible AI practices.

• Expertise

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