About Foundation Health
Foundation Health is transforming healthcare through an AI-powered digital pharmacy platform that seamlessly connects operational infrastructure with high quality patient experiences. Our mission is to transform patient centric care by connecting fragmented infrastructure, optimizing care coordination, and removing friction from the patient journey. We refuse to adhere to the status quo; instead, we actively pioneer solutions that will shape the healthcare practices of tomorrow.
This ambitious vision is only achievable with the dedication of the right team propelling us forward. We firmly believe that a supportive and inspiring work environment fuels creativity, transforming it into groundbreaking innovation. It is this very innovation that not only benefits our organization but also positively impacts our people, partners, and most importantly, our patients.
At Foundation Health, we foster a culture that encourages our team members to broaden their horizons, urging them to bring their passion and curiosity to the workplace each day. We understand that diverse perspectives fuel progress, and we actively seek individuals who share our commitment to excellence and forward-thinking.
The Role
We are looking for a hands-on AI/ML systems expert to partner with our engineering and product teams - as we scale production AI systems across the company.
You’ll focus on strengthening and extending existing AI solutions, providing architectural perspective - whilst helping guide the next phase of scalability and operational maturity across our AI platform.
You will work closely with leadership and engineers on real, deployed systems - contributing both strategic guidance and hands-on execution.
This is an architecture- and execution-oriented role for an experienced AI/ML practitioner who has built and operated production-grade AI systems.
You will collaborate across multiple product areas, including:
Voice AI systems (real-time, low-latency, conversational workflows)
LLM-driven workflow automation
Applied ML and MLOps practices (deployment, monitoring, evaluation, governance)
The goal is not research, but building and operating high-quality AI systems in production.
What You’ll Work On
System Architecture & AI Platform Evolution
Partner with engineering teams to review and evolve existing AI architectures
Provide input on orchestration patterns, tool calling, routing, fallbacks, and escalation logic
Help identify opportunities to improve robustness, scalability, and maintainability
Applied AI Quality & Evaluation
Collaborate on how we measure AI performance in real-world workflows
Help design evaluation approaches such as offline evals, regression suites, golden datasets, and human-in-the-loop review
Define and refine success metrics (accuracy, completion rate, time-to-resolution, safety)
MLOps & Production Readiness
Contribute to best practices around:
deployment patterns
versioning and reproducibility
observability (logs, traces, model outputs)
incident response and rollback strategies
Support monitoring approaches for:
model drift and performance degradation
hallucination and error patterns
latency and cost optimization
Security, Compliance, and Data Handling
Provide architectural guidance aligned with healthcare and regulated-data requirements
Review approaches to:
PHI handling
access controls
auditability
data retention and logging strategies
What You’ll Deliver
Clear, actionable guidance to engineering and leadership evolve and scale our AI systems
Prioritized recommendations to accelerate near-term execution while supporting long-term platform maturity
Architecture guidance for scaling AI workflows reliably across product lines
A pragmatic roadmap (2 weeks / 6 weeks / 3 months) aligned to business priorities
Hands-on collaboration and pairing with engineers to support implementation
Required Experience
8+ years in software engineering, ML engineering, or AI systems
Proven experience delivering AI systems into production
Deep familiarity with LLM-based systems (tool calling, orchestration, guardrails)
Strong grounding in MLOps best practices (monitoring, evaluation, deployment)
Experience designing systems with high reliability requirements
Ability to communicate clearly across engineering and leadership
Strongly Preferred
Experience with healthcare workflows
Experience in regulated data environments (HIPAA, SOC 2, auditability)
Experience building real-time or low-latency systems (Voice AI a plus)
Experience with human-in-the-loop workflow automation
