AI Deployment Manager
Role Overview
The role owns client deployment from pilot through go-live, stabilization, and expansion.
The team works with engineering, data science, operations, and quality control on pilots and live clients.
The role combines client problem solving, design thinking, debugging, and operational efficiency.
Key Skills for This Role
Full Job Posting
Company and platform
RapidClaims provides an AI revenue-cycle platform that codes medical records, improves clinical documentation, and manages denials across more than 25 specialties.
The company describes its platform as achieving 98%+ coding accuracy and serving FQHCs, physician groups, community health centres, and hospital systems.
The company states it has raised $11 million from investors including Accel and Together Fund.
Role purpose and team
The role owns client deployment from pilot through go-live, stabilization, and expansion.
The team works with engineering, data science, operations, and quality control on pilots and live clients.
The role combines client problem solving, design thinking, debugging, and operational efficiency.
What you will own
- Run pilots, take clients live, stabilize deployments, and lead expansion; own the client outcome end to end.
- Translate client needs into prioritized internal requests and convert progress into actionable client updates.
- Trace issues across data, workflows, models, and people, route them to owners, and drive resolution.
- Build, deploy, and measure agents, prompts, and automations that remove manual steps.
- Turn patterns across clients into product features and process changes.
First 45 days
- By day 15, understand the platform and client revenue-cycle workflow, shadow an account, join client calls, and map bottlenecks.
- By day 30, run a pilot covering discovery, data and integration scoping, workflow setup, and an initial quality review; ship a tool that saves team time.
- By day 45, own a client portfolio, have an automation in daily use, and have driven a product or process change based on a client pattern.
Success measures
- Time from pilot start to go-live and from go-live to stable, predictable weekly volume.
- On-time client volume and speed of clearing stuck work.
- Client accuracy and quality outcomes, and timely escalation of model-related issues to data science.
- Expansion across client specialties, sites, and workflows.
- Manual effort removed through tools and automation.
What you bring
- 2+ years of experience and at least one shipped AI solution used by real users, such as an agent, automation, internal tool, or product feature.
- Hands-on prompting, agents, and evaluations; able to explain what was built, how it was tested, and what failed.
- Learn healthcare billing and medical coding quickly; prior domain experience is not needed.
- Structured MECE problem solving, comfort with changing details, and ability to use SQL, Python, or advanced spreadsheets independently.
- Clear written and spoken communication, context switching, high ownership, and a problem-solving approach.
Good to have
- Exposure to healthcare, billing, or US insurance; prior client-facing or startup operations experience.
- Familiarity with agent frameworks, structured outputs, tool calling, or LLM evaluation tools.
Benefits
- Paid time off.
- Provident Fund.
Work location
- Work location is in person; the application asks about willingness to work in the Koramangala, Bangalore office.
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