HHAeXchange - Platform Engineer
Requirements
• Travel up to 10%, including overnight travel • Required Education, Experience, Certifications and Skills • 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) • 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. • The base salary range for this US-based, full-time, and exempt position is $110,000-120,000/yr, not including variable compensation. An employee’s exact starting salary will be based on various factors including but not limited to experience, education, training, merit, location, and the ability to exemplify the HHAeXchange core values. • This is a benefits-eligible position. HHAeXchange offers competitive health plans, paid time-off, company paid holidays, 401K retirement program with a Company elected match, including other company sponsored programs.
Responsibilities
• 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.
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