Sr. Engineering Manager

Arintra • Medical AI • Revenue Cycle & Medical Billing • Java • 2 months ago

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Salary

≈ $30k – $82k per year (Estimated)

Location

In office (Bengaluru)

Seniority

Senior · 9+ years exp

First seen by Alion on Aug 25, 2026.

Overview

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Arintra

Arintra is a healthcare technology company based in Austin, Texas, and founded in 2019. The company provides an AI-driven autonomous medical coding and revenue assurance platform that integrates with electronic health record systems to automate insurance claim submissions. It operates across more than 23 medical specialties and multiple care settings, serving large health systems and provider groups throughout the United States.

arintra.com • HQ: Austin, United States • Medical AI • Revenue Cycle & Medical Billing • 51-200 employees • Est. 2019

Tech stack of Arintra

Backed by Y Combinator, Peak XV Partners, Spider Capital

Responsibilities:

Lead, mentor, and grow a team of 6-10 backend/Platform Engineers.
Own team delivery, roadmap execution, engineering quality, and production reliability.
Drive the adoption of Agentic SDLC across design, development, code review, testing, deployment, and production support.
Define engineering standards, AI code quality gates, and governance for AI-assisted development.
Enable AI-powered developer workflows using Claude Code, Cursor, GitHub Copilot, Codex, Devin, or similar tools.
Track and improve key engineering metrics such as cycle time, code quality, defect escape rate, test coverage, and MTTR.
Partner with Product, Data, and Engineering teams on architecture, technical strategy, and delivery.

Requirements:

9+ years of software engineering experience with 3+ years leading engineering teams.
Strong expertise in Java, Spring Boot, microservices, system design, and distributed systems.
Recent hands-on experience (last 12 months) leading Agentic SDLC / AI-assisted software development.
Experience with AI coding tools such as Claude Code, Cursor, GitHub Copilot (Agent Mode), Codex, Devin, or equivalent.
Strong understanding of LLMs, Prompt Engineering, Context Engineering, MCP, and AI engineering best practices.
Proven experience driving technical architecture, mentoring engineers, and delivering scalable, high-quality software.

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