Platform Engineer

HHAeXchange is the leading technology platform for home and community-based care. Founded in 2008, HHAeXchange was born out of an idea to create a fully comprehensive end-to-end homecare solution to help people who are aging or have disabilities thrive in their homes and communities. Our employees are passionate about transforming the healthcare space by building the only homecare ecosystem that fully connects patients, personal care providers, managed care organizations, and states.

HHAeXchange is seeking a Platform Engineer to join our Data & AI Engineering team. Reporting to the Director of Analytics, this role sits at the intersection of platform reliability and delivery automation – ensuring the infrastructure that powers our AI platform, data pipelines, and internal applications is stable, scalable, and continuously improving.

This is a hands-on engineering role embedded within a team building Layer 1 of HHAeXchange’s AI Platform – the core machinery that enables AI-powered capabilities across the organization. The engineer will own the reliability and deployment lifecycle of that infrastructure, working closely with AI Platform Engineers and Data & AI Engineers to operationalize everything we build on AWS.

As our AI platform matures and internal tooling expands, this role becomes the connective tissue between development velocity and production stability – ensuring that what gets built gets shipped reliably, monitored proactively, and scaled confidently.

To perform this job successfully, an individual must be able to perform each essential job duty satisfactorily with or without reasonable accommodation. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Position is remote for candidates located within the EST or CST time zones within the US.

Platform Reliability (SRE)

• Own availability, latency, and performance targets for AI platform services and data infrastructure running on AWS
• Design and implement monitoring, alerting, and observability frameworks across the platform stack
• Lead incident response, root cause analysis, and post-mortem processes for platform-level outages or degradations
• Define and track SLOs/SLAs for core platform primitives including RAG pipelines, agent orchestration services, and model access layers
• Proactively identify reliability risks and drive engineering improvements before they become production issues
• Build and maintain runbooks, disaster recovery procedures, and operational documentation

DevOps & Delivery Automation

• Design, build, and maintain CI/CD pipelines for AI platform components, data pipelines, and internal applications
• Own infrastructure-as-code (IaC) practices across the team using tools such as Terraform or AWS CDK
• Manage and optimize AWS environments including ECS, Lambda, S3, RDS, Redshift, API Gateway, and related services
• Implement and enforce security, compliance, and cost optimization best practices across AWS infrastructure
• Automate deployment, scaling, and configuration management to reduce manual operational overhead
• Partner with AI Platform Engineers to containerize and operationalize AI services and agent frameworks
• Support Data & AI Engineers with environment management, access controls, and deployment tooling for Polaris and data pipeline infrastructure

Cross-Team Enablement

• Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
• Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function

Other duties as assigned by supervisor or HHAeXchange leader.

• 3+ years of professional experience in a DevOps, SRE, or platform engineering role
• Hands-on AWS experience required – AgentCore, Bedrock, ECS, Lambda, S3, RDS, Redshift, CloudWatch, IAM, VPC, and related services
• Experience with infrastructure-as-code tools such as Terraform or AWS CDK
• Strong CI/CD experience with tools such as GitHub Actions
• Experience with containerization and orchestration (Docker, ECS, or Kubernetes)
• Familiarity with AI/ML infrastructure patterns – model serving, vector databases, pipeline orchestration (strongly preferred)
• Experience with observability and monitoring tooling (Datadog, CloudWatch)
• Prior experience in a SaaS environment
• Strong verbal and written communication skills with ability to collaborate across technical and non-technical stakeholders
• Self-starter with a proactive approach to identifying and resolving infrastructure risk before it impacts delivery
• Willingness to explore and adopt AI tools responsibly to enhance productivity and innovation in your role.