Principal AI Engineer

What You'll Do

• Partner with leaders across business units to identify, prioritize, and sequence high-leverage AI opportunities.
• Embed with teams to understand workflows in detail and translate them into practical automations, copilots, agents, and decision-support tools.
• Design, build, and deploy internal AI-enabled solutions using enterprise platforms such as Claude Code, Glean, retrieval-based systems, agentic workflows, and orchestration frameworks.
• Create reusable patterns, prompts, skills, templates, runbooks, and reference implementations to help business teams replace repetitive administrative effort with AI-assisted workflows.
• Work closely with engineering, data, security, compliance, and clinical stakeholders to ensure AI solutions are safe, governed, maintainable, and appropriate for regulated healthcare environments.
• Influence enterprise AI tool selection, evaluation, rollout, and usage standards based on hands-on experience and measurable business impact.
• Train and enable both technical and non-technical users on how to use AI tools effectively, responsibly, and with the right expectations for quality, risk, and oversight.
• Communicate complex AI concepts clearly to executives, clinicians, operators, and frontline teams, translating technical tradeoffs into plain language and practical decisions.
• Measure and communicate the impact of AI deployments using operational, productivity, quality, and user adoption metrics.
• Serve as an internal evangelist for AI transformation by leading workshops, demos, and office hours; continuously improve how Strive works with AI by sharing lessons learned and recommending where to invest next.
• Meet in person with internal and/or external stakeholders to facilitate team and business priorities/opportunities.

Minimum Qualifications

• Bachelor's Degree in computer science, engineering, data science, or a related technical field, or equivalent practical experience.
• 8+ years of experience in software engineering, machine learning engineering, data engineering, solutions engineering, or a closely related field.
• 2+ years of hands-on experience building and deploying AI-powered systems, including generative AI, retrieval-augmented systems, agentic workflows, or AI-enabled automations.
• Demonstrated ability to operate as a senior individual contributor in ambiguous environments, independently driving work from problem framing through deployment and adoption.
• Strong software engineering fundamentals with practical experience building production-quality tools, services, or workflows.
• Experience partnering directly with business stakeholders to translate operational pain points into technical solutions.
• Ability to communicate clearly with both technical and non-technical audiences and to influence without formal authority.
• Internet Connectivity - Min Speeds: 3.8Mbps/3.0Mbps (up/down); Latency.

Preferred Qualifications

• Experience working in healthcare, value-based care, or another regulated environment with meaningful privacy, security, and governance requirements.
• Strong hands-on experience with enterprise AI platforms and tools such as Claude Code, Glean, coding assistants, agent frameworks, and workflow orchestration tools.
• Experience building internal AI copilots, automation tools, knowledge systems, or agentic applications that support operational or clinical teams.
• Strong Python skills and experience with cloud-based data and application workflows, ideally in AWS.
• Practical experience with retrieval-augmented generation, prompt and context design, evaluation methods, and guardrails for production AI systems.
• Experience operating in a forward-deployed, solutions engineering, field engineering, or internal consulting model where speed, judgment, and stakeholder trust are critical.
• Track record of influencing tool standards, implementation patterns, and adoption practices across multiple teams.
• Experience designing and delivering training, workshops, or enablement programs for users with a wide range of technical backgrounds.
• Experience evaluating AI vendors, tools, and architectures with a pragmatic lens on value, risk, usability, and maintainability.
• Familiarity with healthcare data, clinical workflows, and collaboration with compliance, security, and legal stakeholders.