Staff AI Engineer

Modernizing Medicine; Boca Raton (FL); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical benefits

401(k) match

Paid time off & parental leave

Life & disability benefits

FSAs

Professional development

Hybrid/remote options

Dog-friendly HQ

Catered meals

Job summary

Modernizing Medicine is seeking a Staff AI Engineer to define and drive the architecture of AI and agentic systems across multiple teams and product domains. This senior IC role shapes multi-agent orchestration, production RAG, tool and MCP integration, and the observability stack, while mentoring senior engineers and representing AI engineering in strategic initiatives.

The role emphasizes shipping production agentic AI capabilities, with a background in classical ML as an asset.

Qualifications

• Master’s or Ph.D. degree in Computer Science, Software Engineering, or a related field.
• 10+ years of professional experience in ML/AI or software engineering, including 4+ years in senior or staff-level roles with production system ownership.
• Demonstrated engineering leadership, including driving technical strategy and influencing cross-team decisions.
• Expertise in platform and distributed-systems architecture at scale: model serving, APIs, data platforms, and AI/LLM infrastructure.
• Hands-on experience architecting and operating production agentic AI or LLM systems (multi-agent workflows, production RAG, tool and MCP integration).
• Deep understanding of embedding models, retrieval algorithms, and vector database internals.
• Strong production debugging, reliability, and incident-response skills.
• Experience building rigorous evaluation for non-deterministic AI systems, including statistical methods (such as bootstrap confidence intervals and minimum effect-size thresholds) to separate genuine quality changes from run-to-run model variance
• Cost-awareness for cloud AI/LLM workloads: capacity planning and cost optimization
• Proven mentorship of mid-level and senior engineers.
• Strong communication skills for executive and cross-functional audiences.

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

Skills

ML/AI engineering

Distributed systems

Leadership

Production systems

Executive communication

Mentorship

Cost optimization

Education

Master’s or Ph.D. in CS/SE or related field

10+ years in ML/AI or software engineering

4+ years in senior or staff roles

Tools

Model Context Protocol (MCP)

LLM platforms

Inference platforms

Job description

Job Overview

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 thresholds (hard‑gate vs monitor components) wired into CI so a measurable quality regression blocks the release, with observability via tracing across multi‑step chains and tool calls and drift detection on LLM inputs and outputs
• Drive LLM cost optimization at scale: model routing, caching, batching, token budget management, and provider cost analysis

Required Skills & Qualifications

• Master’s or Ph.D. degree in Computer Science, Software Engineering, or a related field
• 10+ years of professional experience in ML/AI or software engineering, including 4+ years in senior or staff‑level roles with production system ownership
• Demonstrated engineering leadership, including driving technical strategy and influencing cross‑team decisions
• Expertise in platform and distributed‑systems architecture at scale: model serving, APIs, data platforms, and AI/LLM infrastructure
• Hands‑on experience architecting and operating production agentic AI or LLM systems (multi‑agent workflows, production RAG, tool and MCP integration)
• Deep understanding of embedding models, retrieval algorithms, and vector database internals
• Strong production debugging, reliability, and incident‑response skills
• Experience building rigorous evaluation for non‑deterministic AI systems, including statistical methods (such as bootstrap confidence intervals and minimum effect‑size thresholds) to separate genuine quality changes from run‑to‑run model variance
• Cost‑awareness for cloud AI/LLM workloads: capacity planning and cost optimization
• Proven mentorship of mid‑level and senior engineers
• Strong communication skills for executive and cross‑functional audiences

Preferred Qualifications (Nice to Have)

• Experience in Healthcare, FinTech, or other regulated industries
• Experience building AI/LLM systems or platform components from the ground up
• Defined best practices for AI‑assisted development (Claude Code): code quality standards, review, and responsible usage across teams
• Track record of conference talks, published papers, or significant open‑source contributions
• Experience with GPU‑accelerated inference and model serving optimization
• Familiarity with workflow orchestration and streaming architectures for real‑time AI

Benefits

• Comprehensive medical, dental, and vision benefits, including a company Health Savings Account contribution
• 401(k): ModMed provides a matching contribution each payday of 50% of your contribution deferred on up to 6% of your compensation. After one year of employment, 100% of any matching contribution you receive is yours to keep
• Generous Paid Time Off and Paid Parental Leave programs
• Company‑paid Life and Disability benefits, Flexible Spending Account, and Employee Assistance Programs
• Company‑sponsored Business Resource & Special Interest Groups that provide engaged and supportive communities within ModMed
• Professional development opportunities, including tuition reimbursement programs and unlimited access to LinkedIn Learning
• Global presence and in‑person collaboration opportunities; dog‑friendly HQ (US), Hybrid office‑based roles and remote availability for some roles
• Weekly catered breakfast and lunch, treadmill workstations, Zen, and wellness rooms within our BRIC headquarters