Lead Platform Engineer

Lead Platform Engineer

Department: Engineering

Location: Remote

FLSA Status: Exempt

People Leader: Yes

Travel: Up to 5%

Summary

Company Summary

Accuity partners with hospitals and health systems through a technology-enabled, physician-led model that improves clinical documentation integrity, coding accuracy, reimbursement optimization, and quality outcomes.

Job Summary

The Lead Platform Engineer is a senior, hands-on technical leader responsible for the direction, reliability, and continued evolution of Accuity's internal engineering platform. The role enables software engineering and data science teams to deploy and operate production workloads in Microsoft Azure through reliable, repeatable, secure, and increasingly self-service capabilities

This position owns the shared infrastructure and delivery pathways that support application teams while preserving clear accountability for application behavior, quality, and production ownership within those teams. The Lead Platform Engineer establishes the platform roadmap, mentors engineers and Software Development Engineers in Test (SDETs), defines technical standards, and contributes directly to high-impact platform design and implementation.

Responsibilities

Platform Strategy and Technical Leadership

• Define and maintain a focused platform roadmap based on recurring delivery, reliability, and operational challenges experienced by software engineering and data science teams.
• Establish technical priorities, engineering standards, architectural direction, and sustainable ownership practices for platform capabilities.
• Provide hands-on technical leadership and mentorship to platform engineers, SDETs, and other engineers contributing to platform initiatives.
• Evaluate which capabilities should be centralized within the shared platform and which should remain owned by application or data science teams.
• Lead technical design discussions, review critical changes, and guide decisions involving platform architecture, reliability, security, scalability, and maintainability.
• Promote a pragmatic, service-oriented platform model that creates leverage for engineering teams without introducing unnecessary approval gates or operational dependencies.


Cloud Platform Engineering
• Build, maintain, and improve Accuity's Azure-based internal developer platform.
• Develop and support infrastructure-as-code solutions for repeatable, secure, and consistent environment provisioning.
• Own shared platform capabilities related to configuration, secrets management, cloud identity, network connectivity, CI/CD, deployment automation, and operational tooling.
• Design platform services and workflows that reduce manual effort, minimize configuration drift, and improve deployment consistency.
• Ensure platform capabilities are resilient, supportable, appropriately governed, and aligned with organizational security and compliance requirements.

Environment and Release Enablement

• Own the lifecycle and consistency of development, test, staging, and production environments.
• Manage shared standards and capabilities for provisioning, configuration, access, drift management, deployment promotion, rollback, and environment support.
• Create supported, documented, self-service deployment pathways that enable software engineers to release and operate the applications they own with reduced manual platform intervention.
• Partner with software engineering teams to improve release reliability while maintaining application-level quality and release accountability within those teams.
• Identify recurring delivery activities and convert them into reusable, automated, and maintainable platform capabilities.

AI and Data Science Infrastructure

• Provide and maintain the Microsoft Foundry and Azure infrastructure used to deploy production AI workloads.
• Support repeatable provisioning, access controls, network configuration, capacity management, monitoring, promotion, and rollback for AI-related environments.
• Partner with Data Science to understand infrastructure and deployment requirements while maintaining clear boundaries between platform responsibilities and ownership of models, evaluations, and data science outcomes.
• Help establish secure, scalable, and operationally supportable patterns for deploying AI capabilities in production.

Quality, Observability, and Reliability

• Guide SDETs toward reusable automated validation and test infrastructure that integrates with the shared delivery platform.
• Maintain clear ownership boundaries so that application quality and release decisions remain with the appropriate software teams.
• Provide shared deployment and opera