Applied ML & Ops Consultant

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