Applied ML & Ops Consultant

Overview

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.

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.

• 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

• 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)

• 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

• Provide architectural guidance aligned with healthcare and regulated-data requirements

• Review approaches to:

  • PHI handling

  • access controls

  • auditability

  • data retention and logging strategies

• 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

• 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

• 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