Vice President AI/ ML

About the role

AI is now central to how patients reach care at DexCare. Conversational agents talk to patients by voice and text. Predictive models decide where capacity should go. Knowledge systems turn a health system's undocumented operational rules into something machines can act on. All of it is live with US customers today.

We're hiring a VP of AI/ML to own that work end to end, reporting to the CTO. You'll set technical direction for AI/ML across the company, build and scale the ML engineering team in US / India, and be accountable for how these systems behave in production — where a wrong answer to a patient is a safety event, not a bad demo.

This is a senior leadership role for an engineer at heart. You'll build and manage a team and still be expected to review architecture, read code, and hold a real opinion about model behavior. You'll work closely with engineering, product, and data leaders based in the US.

Conversational AI. The engine behind patient-facing voice and text agents — orchestration, dialogue design, and the balance between model-driven flexibility and the deterministic control healthcare demands.

Realtime voice. Latency, concurrency, and conversation quality on live telephony at production volume.

Predictive ML. Demand, capacity, ranking and matching problems across the scheduling and access surface.

Retrieval and knowledge. Extracting structure from clinical and operational material that was never meant to be machine-readable, and serving it back to agents and applications reliably.

Safety and reliability. Classification and monitoring that runs alongside every patient conversation, plus the evaluation systems that decide whether a change is safe to ship.

The full lifecycle. Experimentation through deployment, monitoring, and the MLOps practice underneath it.

Model strategy and economics. Build-versus-buy across model providers, and the unit cost of every interaction as a metric you manage deliberately.

The team. Hiring, structuring and growing the ML organization in US / India as part of a global function.

You've shipped LLM-based systems into production under real regulatory constraint — healthcare, financial services, or somewhere else a wrong answer has consequences.

Beyond that:

Typically 15+ years across machine learning, AI, software engineering or data science, with a hands-on engineering foundation

Deep Python and modern ML tooling; PyTorch, TensorFlow or equivalent

Realtime or streaming voice systems: latency budgets, interruption handling, telephony

Retrieval systems built over messy enterprise knowledge

Evaluation harnesses that gate releases

Experience hiring and leading ML engineering teams and senior technical talent, ideally including building a team from a small base

Multi-tenant SaaS, with a working understanding of tenant isolation and per-customer configuration

Production experience on Azure or AWS

Comfort operating in a global structure with US-based peers and customers, including overlap hours

Healthcare data fluency — HIPAA, PHI, or equivalent regulated-data experience — or the appetite to get there fast

Advanced degree or an equivalent track record. Patents and publications welcome, not required.

How we work

Spec-driven, evals-driven, and AI-assisted in our own engineering practice. Flat teams with senior engineers embedded rather than long management chains. We move fast, we write things down, and we expect leaders to argue their positions and change their minds when the argument goes the other way.