Director, Software Engineering - Developer Platforms and AI

GHX

About The Position

GHX is building a new engineering capability to transform how our teams build and deploy healthcare supply chain solutions. As Director of Developer Platform & AI Enablement, you'll lead a small, high-impact team that embeds directly with product engineering teams to drive measurable productivity gains through AI-assisted development practices and modern cloud-native infrastructure patterns. This is a player-coach role. You'll spend the majority of your time (70-80%) embedded with application teams, writing code, architecting solutions, and proving out patterns. You'll pioneer how GHX leverages AI throughout the software development lifecycle while simultaneously playing a key role in our evolution toward cloud-native architecture and team-owned infrastructure. You'll be the technical leader who shows teams "what good looks like" rather than telling them—demonstrating measurable productivity gains through hands-on execution while building reusable patterns that scale across the organization.

Requirements

10+ years of software engineering experience with at least 3 years in technical leadership roles
Deep hands-on experience with cloud-native architecture on AWS (containers/ECS/EKS, serverless, managed databases, event-driven patterns)
Strong infrastructure-as-code background (Terraform, CloudFormation, or CDK) with experience enabling engineering teams to own their infrastructure
Demonstrated experience integrating AI/LLM tools into software development workflows—whether through custom tooling, MCP servers, or commercial platforms (GitHub Copilot, Claude Code, Cursor, etc.)
Track record of embedding with teams to drive technical transformation and measurable productivity improvements
Excellent communication skills with ability to influence and mentor engineers across all levels
Comfortable working in a player-coach capacity—rolling up sleeves while providing strategic direction

Nice To Haves

Experience in healthcare, regulated industries, or complex B2B environments
Background in platform engineering, developer experience, or engineering enablement
Familiarity with measuring developer productivity (DORA metrics, flow metrics, qualitative feedback)
Experience building internal developer platforms or establishing engineering best practices at scale
Understanding of security, compliance, and governance requirements in cloud environments

Responsibilities

Embed directly within product engineering teams to drive measurable improvements in developer productivity by integrating AI into daily workflow design, coding, testing, deployment, and operations
Lead tool selection and adoption strategy for AI development platforms (GitHub Copilot, Claude Code, Cursor, etc.), establishing best practices for prompt engineering, context management, and workflow integration
Build and maintain custom AI tooling, MCP servers, and integrations that provide teams with domain-specific context from your codebase, documentation, infrastructure, and business systems
Develop and socialize reusable patterns for code generation, refactoring, test automation, incident analysis, and knowledge retrieval that teams can apply across their daily work
Implement AI code review and validation practices to ensure AI-generated code meets security, quality, and HIPAA compliance standards
Champion a culture of AI-augmented development that enables engineers to tackle bigger challenges, improve code quality, and reduce toil—without sacrificing maintainability or creating technical debt
Create self-service documentation, learning paths, and enablement programs (workshops, office hours, communities of practice) to scale AI adoption across engineering
Measure and communicate impact using both quantitative metrics (velocity, quality, time-to-deployment) and qualitative measures (developer satisfaction, cognitive load reduction, friction logging)
Lead application-level infrastructure-as-code initiatives, empowering product teams to own their cloud resources (containers, SQS, Lambda, DocumentDB, DynamoDB, etc.)
Design reference architectures and terraform/CDK patterns that balance team autonomy with consistency and security
Partner with the enterprise DevOps team to establish clear boundaries—they own foundational infrastructure (VPCs, security groups, IAM foundations), you enable teams to own application-layer resources
Build proof-of-concepts and migration playbooks as GHX transitions from lift-and-shift EC2 environments to truly cloud-native architectures
Manage engineers on cloud-native design patterns, observability, cost optimization, and operational excellence
Provide direction, hands-on execution to grow an engineering team
Work closely with product managers, engineering leaders, and cross-functional partners to translate business needs into practical AI and infrastructure solutions
Establish metrics and feedback loops to continuously improve both AI adoption and cloud-native maturity
Balance innovation with pragmatism—championing new approaches while ensuring solutions scale sustainably