Deployment Specialist

Arya Health•New York, NY

About The Position

Own the implementation of customer engagements from enterprise discovery through go-live and the first 30–60 days of adoption. You are the bridge between Sales and the Customer Success Manager—responsible for discovering what the customer truly needs, designing the solution, executing the implementation, and ensuring the customer successfully adopts and realizes value. Arya is a single AI agent platform with multiple skills customers deploy individually or combined. Your role is to discover the customer's workflow reality, design the right skill combinations, manage the technical execution, coordinate across teams, and own whether the customer goes live successfully and starts seeing value. Your core focus is four overlapping phases: Enterprise Discovery & Solutioning — Map actual workflows, systems, and data. Identify the highest-impact automation opportunities. Design how agents solve problems. Build customer conviction that the solution will work. Kickoff & Planning — Translate discovery into a detailed implementation plan. Clarify scope, timeline, risks, and success criteria. Execution & Coordination — Lead implementation day-to-day. Coordinate across your team, engineering, and the customer. Troubleshoot issues and make trade-off decisions in real time. Go-Live & Early Adoption — Own go-live execution and post-go-live support. Ensure the customer is trained, using the solution, and seeing projected value. Hand off to the CSM with a stable, adopting customer. You are an implementation operator who discovers, designs, executes, and owns outcomes that drive ongoing customer value.

Requirements

You have led discovery, designed solutions, and managed complex implementations while remaining comfortable with ambiguity and quick decision-making.
You are familiar with healthcare operational constraints and decision-making and can speak the healthcare language effectively.
You possess hands-on experience implementing automation platforms and understand implementation constraints and common blockers.
You understand data mapping, ETL, APIs, and system integrations, allowing you to troubleshoot issues, trace problems, and escalate accurately so that customers and engineering trust your expertise.
You have managed customers through delays, blockers, and scope changes by delivering news clearly and helping them make trade-off decisions to build confidence in messy situations.
You know when to escalate issues versus solving them yourself and can make quick trade-off decisions regarding scope, timeline, and quality while remaining comfortable with ambiguity.

Nice To Haves

Healthcare EMR, HRIS, or scheduling system experience (Well например, WellSky, AxisCare, KanTime, ADP).
Healthcare compliance knowledge (HIPAA, HITRUST).
SQL, Python, or data work experience.
Healthcare data integration or ETL experience.
Skill-based or modular software platform experience.

Responsibilities

Lead structured discovery to uncover actual workflows, data quality, constraints, and automation opportunities.
Evaluate which workflows fit Arya and where multiple skills combine for greater value.
Design solutions showing how skills solve customer priorities.
Map data and integrations; communicate technical complexity clearly.
Build conviction upfront so customers are bought in before execution starts.
Lead kickoff meetings and translate discovery into a detailed project plan.
Confirm scope, timeline, risks, dependencies, and success criteria.
Set up governance (standups, status reporting, escalation paths).
Ensure the customer and internal team understand what's being built and the timeline.
Coordinate across the implementation team, engineering, and product.
Maintain customer confidence through standups, issue resolution, and regular communication.
Translate customer needs to engineering; translate engineering constraints back to customers.
Help customers redesign workflows around Arya's constraints instead of shutting down ideas.
Manage expectations through delays, blockers, and technical realities while explaining trade-offs clearly.
Own UAT planning and track issues and go-live readiness.
Plan and execute go-live; manage cutover and monitor for issues.
Stay with the customer 24–48 hours post-go-live for stability and troubleshooting.
Own the customer through the first days post-launch.
Verify the solution is delivering value and the customer is adopting it.
Identify and fix adoption blockers, always improving the deployment motion.
Gather feedback on what works and what gaps exist, and communicate those insights to Engineering and Product to help inform the product roadmap.
Hand off to the AI Support and Strategy Manager with a successful implementation.