Lead AI Engineer

Gifthealth is revolutionizing healthcare by simplifying prescription management and health services. The Lead AI Engineer will build and scale the AI engineering function, leading a team to implement advanced AI systems while collaborating with cross-functional teams.

Responsibilities:

• Designs and builds agentic AI systems including LLM orchestration, RAG pipelines, and multi-agent frameworks
• Leads, mentors, and grows AI Engineers, conducting one-on-one meetings, performance reviews, and identifying career development opportunities
• Works with the Talent Acquisition team to recruit and hire AI Engineers through defining role requirements, screening candidates, and leading technical interviews
• Establishes engineering practices, coding standards, and technical processes for AI systems
• Partners with ML Engineers, Prompt Engineers, and Product on cross-functional AI initiatives
• Drives build/buy/integrate decisions; evaluate AI tooling and vendor solutions

Requirements:

• Bachelor's degree in computer science, AI/ML, or a related field OR 2–4 years of equivalent practical experience
• 6+ years of software engineering experience with 3+ years in AI/ML systems; 2+ years of management or tech lead experience; history of building and shipping production AI systems
• Knowledge of LLM architectures and orchestration patterns; RAG systems and vector databases; production AI/ML system design; software engineering best practices; and leadership and management principles
• Python and AI/ML development skills
• LLM API integration skills
• System architecture and design skills
• Technical hiring and interviewing skills
• Team leadership and mentorship skills
• Ability to balance hands-on building with team leadership
• Ability to hire and develop engineering talent
• Ability to drive technical decisions across teams
• Ability to communicate with technical and non-technical stakeholders
• Master's degree in computer science or AI/ML
• Experience in healthcare or regulated industries; history of building teams from scratch; FDA or clinical trial software experience
• Knowledge of the healthcare domain and HIPAA compliance; FDA software validation; human-in-the-loop ML systems; graph databases and knowledge graphs
• GPU-accelerated processing skills
• Distributed systems skills
• Cloud platform (AWS, GCP) skills
• ML Ops tooling skills
• Ability to navigate ambiguity in early-stage product development
• Ability to establish engineering culture and practices from scratch
• Ability to influence without authority across functions