M
Staff AI Engineer
Modernizing Medicine, Inc
5 hours ago
Full-time
On-site
Boca Raton, Florida, United States
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
#J-18808-Ljbffr
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
#J-18808-Ljbffr