Principal AI Engineer(5+ years)

Summary: The Principal AI Engineer serves as a technical authority and strategic leader in applying advanced Generative AI technologies within the healthcare ecosystem. This role defines architectural direction for enterprise-scale AI platforms, mentors senior engineers, and partners with executive stakeholders to align AI...

General Position Information

Reports directly to (Title):

Leader AI Engineering

Matrix reports to (Title):

AVP AI & ML Platform engineering

Direct Reports:

Individual contributor; no direct reports

Created / Last Revised:

-

Job Code:

-

Position Summary:

The Senior Staff AI Engineer serves as a technical authority and strategic leader in the application of advanced Generative AI technologies, driving enterprise-scale innovation across the healthcare ecosystem. This role is responsible for defining and evolving architectural direction, guiding complex AI initiatives, and ensuring the successful adoption of cutting-edge AI capabilities that transform how healthcare solutions are designed and delivered.

Operating at a level of influence beyond a single team, the Senior Staff AI Engineer shapes the long-term technical roadmap for Generative AI, particularly across GCP Vertex AI, Gemini, agentic systems, and large-scale Retrieval-Augmented Generation (RAG) platforms. The role balances deep hands-on technical contribution with organization-wide technical stewardship, mentoring Staff and Senior engineers, and partnering with executive stakeholders to align AI strategy with business outcomes.

This position sits within a flagship innovation organization and is intended for a highly experienced engineer who consistently operates in ambiguous problem spaces, sets technical standards, and drives durable, scalable AI platforms across multiple initiatives.

Responsibilities:

Technical Leadership & Architecture

• Define and evolve the architectural vision and technical standards for enterprise Generative AI platforms.
• Lead the design of large-scale, multi-team AI systems, ensuring robustness, scalability, security, and responsible AI practices.
• Serve as a final technical reviewer for critical architectural decisions impacting multiple teams or strategic initiatives.
• Establish best practices for LLM usage, RAG architectures, embeddings, evaluation, observability, and lifecycle management.

Advanced AI Systems Development

• Lead the development of enterprise-grade Generative AI solutions on Google Cloud Platform, with deep expertise in Vertex AI, Gemini, and emerging tooling.
• Architect and oversee sophisticated Retrieval-Augmented Generation (RAG) ecosystems grounded in proprietary healthcare data.
• Design and standardize data grounding strategies, schemas, and pipelines that connect LLMs with real-world, real-time data sources.
• Drive the exploration and production adoption of agentic AI systems capable of complex reasoning, orchestration, and autonomous task execution.
• Champion the enterprise adoption and evolution of interoperability frameworks such as the Model Context Protocol.

Organizational Influence & Strategy

• Partner with AI leadership, product leadership, and executive stakeholders to shape AI strategy and long-term investment priorities.
• Translate complex technical capabilities into clear business value propositions and architectural options for senior leadership.
• Influence roadmap planning across multiple teams by identifying shared platforms, reusable components, and systemic risks.

Mentorship & Engineering Excellence

• Act as a technical mentor and sponsor for Staff and Senior Engineers, guiding their growth toward higher-level technical impact.
• Foster a culture of engineering excellence through architecture reviews, design mentorship, and cross-team collaboration.
• Lead investigations into complex, ambiguous AI challenges and guide teams toward elegant, scalable solutions.

Education & Experience:

• Bachelor’s degree in computer science, Engineering, or a related field, or equivalent practical experience.
• 10+ years of professional software engineering experience, with 5–7+ years focused on AI/ML or advanced applied machine learning systems.
• Proven track record of architecting and influencing large, production-grade AI platforms across multiple teams or domains.
• Experience working across multiple stakeholders and managing recurring deliverables in a deadline-driven environment.

Must Have Skills:

• Demonstrated growth mindset with the ability to proactively identify, evaluate, and drive adoption of emerging AI technologies, frameworks, and paradigms across teams and platforms in a rapidly evolving AI landscape.
• Ability to independently operate in highly ambiguous problem spaces, define technical direction, and align engineering outcomes with long-term business strategy.
• Expert-level knowledge and hands-on leadership experience with GCP Vertex AI, including generative AI models (e.g., Gemini), Vertex AI Studio, and enterprise deployment patterns; recognized as a subject matter expert across teams.
• Advanced, architecture-level expertise in designing, scaling, and governing Retrieval-Augmented Generation (RAG) systems, including tradeoff analysis, evaluation frameworks, and long-term maintainability.
• Proven experience defining enterprise standards and best practices for Vector Stores, embeddings, indexing strategies, and lifecycle management at scale.
• Deep expertise in grounding strategies, including data modeling, schema design, and large-scale pipeline orchestration to ensure LLM factuality, traceability, and regulatory alignment.
• Strong leadership-level experience with DevOps and CI/CD for AI/ML systems (MLOps), including platform design, governance, observability, and reliability across environments.
• Extensive experience designing cloud-native architectures for AI platforms, including serverless, containerized, and hybrid solutions with a focus on scalability, resilience, and security.
• Ability to define and review system and platform diagrams (e.g., Visio, architecture artifacts) that communicate complex architectures clearly to technical and executive audiences.
• Strong working knowledge of the Model Context Protocol, with experience guiding its application to enable standardized, extensible interactions between LLMs and external tools and data sources.
• Advanced familiarity with Agentic AI frameworks and patterns (e.g., LangChain), including evaluation of autonomous and multi-agent systems for real-world enterprise use cases.
• Broad and deep full-stack experience across enterprise technology ecosystems, with the ability to architect and integrate solutions involving:
• SQL and NoSQL databases (e.g., SQL Server, CosmosDB, MongoDB).
• ETL and large-scale data pipelines (e.g., GCP DataFlow, Azure Data Factory).
• Eventing and streaming platforms (e.g., Azure EventHub, GCP Pub/Sub, Kafka).
• Microservice and distributed systems (e.g., Docker/Kubernetes, Java, Python, NodeJS).
• Experience architecting and integrating AI solutions within CRM, ERP, eCommerce, or EMR/EHR systems, including enterprise integration patterns, security, and compliance considerations.
• Expert understanding of Agile methodologies and modern software development lifecycles, with the ability to influence process improvements across multiple teams.
• Demonstrated ability to communicate complex technical concepts succinctly and persuasively to engineers, product leaders, and executive stakeholders, both verbally and in writing.
• Ability to lead engineering communities of practice, technical forums, or internal events focused on AI, software engineering excellence, or platform evolution.
• Exceptional problem-solving and analytical skills, with a history of resolving systemic, high-impact technical challenges.
• Advanced troubleshooting expertise, including log analysis, performance tuning, and load testing in large, distributed AI systems.
• Ability to lead through influence, guiding multiple engineers or teams toward outcomes without direct authority.
• Strong ability to understand and shape technical context, including codebases, architectures, and their direct relationship to business objectives and long-term strategy.

Nice to Have Skills:

• Experience implementing and governing Responsible AI, AI safety, model evaluation frameworks, and compliance controls within enterprise environments.
• Experience leading cloud transformation initiatives involving AI/ML platforms, data modernization, and large-scale enterprise application integration.
• Hands-on experience with healthcare data platforms, Electronic Health Records (EHR/EMR), healthcare interoperability standards, or regulated industry environments.
• Experience building and scaling enterprise AI Centers of Excellence (CoE), Communities of Practice, or technical innovation programs across distributed teams.
• Relevant industry certifications in Google Cloud Platform (GCP), Artificial Intelligence/Machine Learning, Cloud Architecture, Kubernetes, or related technologies.

Licenses, Certifications & Training:

N/A

Knowledge, Skills, Abilities, Behaviors:

• Strong strategic and architectural mindset with the ability to define technical vision, influence enterprise-wide AI initiatives, and align technology decisions with business objectives.
• Exceptional leadership, collaboration, and stakeholder management skills, with the ability to communicate complex AI and technical concepts effectively to engineers, product teams, and executive leadership.
• Demonstrated ability to lead through influence, mentor senior engineering talent, and foster a culture of innovation, engineering excellence, continuous learning, and knowledge sharing.
• Strong analytical, problem-solving, and decision-making capabilities with the ability to navigate ambiguity, assess technical risks, and develop scalable, secure, and sustainable AI solutions.
• Results-oriented, adaptable, and customer-focused professional with a growth mindset, strong sense of ownership, and commitment to delivering high-quality, responsible, and business-driven AI outcomes.