Job Overview
Lead Software Engineer - AI Applications
About the Role
This is a unique opportunity to lead a global engineering team responsible for building and deploying AI-first applications.
The ideal candidate will have expertise in architecture oversight, coding, and agile team management to deliver scalable, cost-effective digital solutions.
The successful candidate will ensure reduced vendor dependency, retain intellectual property, and accelerate delivery speed.
Key Responsibilities
* Build and lead an offshore engineering team to deliver enterprise-scale AI-native applications.
* Collaborate with Product Management and Data Science teams to develop and deploy AI-powered solutions for clinicians and patients.
* Establish engineering best practices including continuous integration, test automation, code quality, and cloud-native deployments.
* Drive architectural decisions (microservices, APIs, security, scalability).
* Ensure efficient delivery while maintaining high quality and reliability.
* Act as a hands-on leader, including coding, reviewing, and mentoring team members.
* Align mobile platforms with organizational priorities to enhance patient engagement, provider efficiency, and enterprise growth.
Supervisory Responsibilities
* Lead strategy, design, and execution of all mobile applications and digital tools across iOS, Android, and cross-platform environments.
* Oversee end-to-end development lifecycle, including architecture, security, integration with clinical systems, and user experience.
* Ensure performance, scalability, compliance, and alignment with organizational goals.
Qualifications
* Master's Degree in Healthcare, User Experience, Human-Centered Design, or relevant field preferred.
* 10+ years in software engineering with at least 3 years leading offshore teams.
* Proven track record of delivering high-velocity, cost-effective software solutions.
Skills & Competencies
* Expertise in AI/ML-enabled applications and API-first architectures.
* Proficient in modern cloud tech stacks (AWS, Azure, GCP), Kubernetes, and microservices.
* Strong collaboration with data scientists, engineers, and product stakeholders.
* Ability to translate complex ML concepts into business outcomes.
* Data-driven decision-making using KPIs and telemetry.
* Advocates for ethical AI, bias mitigation, explainable models, and data privacy.
* Ensures compliance with regulatory standards and internal governance frameworks.
Working Conditions
* Full-time remote/telework.
* Computer-based work.
* Occasional travel for leadership meetings and team planning.