AI Engineer

Position: Staff AI Engineer
## Staff AI Engineer Apply remote type:
Hybrid locations:
Boca Raton, FLtime type:
Full time posted on:
Posted Todayjob requisition :
R4761
** Join the Team Modernizing Medicine
** At
** Mod Med**, we’re not just building software—we’re reimagining the healthcare experience. Founded in 2010 by a practicing physician and a successful tech entrepreneur, we took a radically different approach:
** we hired doctors and taught them how to code.
** This "for doctors, by doctors" philosophy has allowed us to create an AI-enabled, specialty-specific cloud platform that places patients at the center of care.
** A Culture of Excellence
** When you join Mod Med, you’re joining an award-winning team recognized for innovation and employee satisfaction.
From our global headquarters in Boca Raton Florida, and extensive employee base in Hyderabad India, we are a team of 4,500+ passionate problem-solvers on a mission to increase medical practice success and improve patient outcomes:
* ** Consistently ranked as a Top Place to Work**
* ** 2025 Globee Business Awards:
** Gold Globee for “Technology Team of the Year”
* ** 2025 Black Book Awards:
** Ranked #1 EHR in 11 Specialties
* ** Florida Venture Forum:
** Venture-Backed Company of the Year We are growing fast, thinking big, and we are just getting started.
** Ready to modernize medicine with us?
**** Job Description

Summary:

** As a
** Staff AI Engineer,
** you define and drive the architecture of AI and agentic systems across multiple teams and product domains. This is a senior individual-contributor leadership role: you influence high-impact architectural decisions, evolve the practices and standards for building agentic AI, and turn experimental AI capabilities into reliable production systems. You set direction for multi-agent orchestration, production RAG (hybrid search, re-ranking, and query routing), tool and MCP integration, and the evaluation and observability stack that keeps them dependable.

You mentor senior engineers and represent AI engineering in cross-functional and strategic initiatives. A background in classical ML is an asset; the primary requirement is a proven track record of shipping production agentic AI.
**** KEY RESPONSIBILITIES
***** Define and drive technical direction for AI and agentic systems, and contribute to the AI platform roadmap across teams
* Influence architecture decisions for compute, cloud, and AI infrastructure across teams
* Lead the design of large-scale AI/LLM systems: inference platforms, APIs, and distributed architectures
* Architect production multi-agent systems end-to-end: orchestration, state management, tool integration, and failure handling
* Define and drive best practices and standards for AI/LLM systems across teams (agent design, evaluation, observability, reliability)
* Lead complex production debugging and incident response across teams, and harden the resulting fixes into platform guardrails
* Mentor senior engineers and emerging technical leaders, raising the engineering bar
* Lead technical design reviews and architecture decision records (ADRs) for critical AI infrastructure
* Contribute to capacity planning and cost optimization strategies for AI/LLM infrastructure
**** GENAI / AGENTIC AI CAPABILITIES
***** Define and drive vector database and RAG architecture decisions across systems and teams: structured RAG, hybrid search (dense + sparse + keyword), re-ranking, and query routing
* Lead multi-agent platform architecture decisions: runtime selection, orchestration patterns, and enterprise integration strategy
* Set the technical direction for MCP (Model Context Protocol) adoption and agent runtime infrastructure
* Shape agent infrastructure adoption: evaluate and standardize frameworks, tooling, and deployment patterns for agentic AI
* Architect evaluation infrastructure for non-deterministic LLM systems: synthetic golden-set generation, hierarchical weighted scoring (component, composite, and system-level F1), bootstrap confidence intervals, and paired A/B comparison, treating a change as real only when it is both statistically significant and clears a minimum effect size
* Gate deployments on eval results: tiered regression…