Vice President AI/ ML
Bengaluru
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s listing: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-Time
Employment type in the employer’s listing: Full-Time
From the employer’s posting
What you’ll work on
Full postingAI is now central to how patients reach care at DexCare.
We're hiring a VP of AI/ML to own that work end to end, reporting to the CTO.
Set technical direction for AI/ML across the company
Build and scale the ML engineering team in US and India
Own conversational AI for patient-facing voice and text agents
Manage predictive ML for demand, capacity, and matching
Own safety, monitoring, and release evaluation for AI systems
From the employer’s posting
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.
You'll set technical direction for AI/ML across the company
build and scale the ML engineering team in US / India
The engine behind patient-facing voice and text agents
Demand, capacity, ranking and matching problems across the scheduling and access surface.
Classification and monitoring that runs alongside every patient conversation, plus the evaluation systems that decide whether a change is safe to ship.
Tools in this posting
- Python
- AWS
- Azure
- PyTorch
- TensorFlow
Source — Tool mentions in context
- 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
- 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
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 14, 2026 · 34 days ago
- Recorded sightings
- 11
- Last seen by us
- Oct 18, 2026
- Employer says posted
- Sep 11, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
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Job description
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.
What you'll own
- 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.
What you'll bring
- 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.
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:
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.Employment type
Full-Time
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bengaluru
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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