Principal Engineer

Principal Engineer - Ai & Platform Architecture

KORE1, a nationwide provider of staffing and recruiting solutions, has an immediate opening for a Principal Engineer - AI & Platform Architecture that is fully remote.

We operate a large-scale, multi-tenant SaaS platform supporting thousands of enterprise customers across the United States. Our backend infrastructure - built on event-driven, serverless AWS architecture - powers critical operational workflows, real-time transaction processing, integrations, and customer lifecycle management , the organization is evolving toward intelligent automation and agentic AI systems: autonomous, tool-using services capable of reasoning over platform state, interacting with production systems, executing workflows, and learning from outcomes within established guardrails.

This Principal Engineer will lead the design and implementation of these AI-driven systems while also shaping the broader architectural direction of the platform. This is a highly hands-on technical leadership role focused on production engineering, system design, AI orchestration, platform scalability, and operational reliability. This individual will establish the engineering patterns, architectural standards, and operational safeguards that enable AI-enabled automation to function safely and effectively in high-scale production environments.

• Large-scale multi-tenant SaaS environment supporting thousands of enterprise customers
• High-volume transaction and integration workloads across distributed systems
• Complex API ecosystems spanning REST, SOAP, event-driven, and vendor-integrated services
• Large-scale data pipelines and cross-domain integrations supporting real-time operations

• Design, build, and operate AI-enabled workflow orchestration systems and intelligent automation services.
• Develop agent infrastructure, tool interfaces, evaluation frameworks, observability standards, and operational guardrails.
• Establish engineering standards for integrating AI systems safely into production environments.
• Drive architectural decisions around model orchestration, workflow automation, scalability, reliability, and cost optimization.

• Lead architecture and migration initiatives across distributed services and platform modernization efforts.
• Design scalable patterns for feature rollouts, traffic management, deployment safety, and service decomposition.
• Own cross-functional architectural concerns including observability, security, identity management, and data architecture.
• Ensure platform reliability, maintainability, and operational resilience across evolving systems.

• Mentor senior engineers and raise engineering standards across multiple development teams.
• Lead technical design reviews, architecture documentation, and engineering decision-making processes.
• Partner cross-functionally with Product, Engineering, Infrastructure, and Leadership teams to align technical strategy with business priorities.
• Help evaluate build-vs-buy decisions across AI, infrastructure, and platform tooling initiatives.

• AWS serverless and distributed systems architecture including Lambda, Event Bridge, DynamoDB, ECS/Fargate, Aurora/RDS, API Gateway, S3, and Cloud Watch
• Infrastructure-as-code and CI/CD environments supporting enterprise-scale deployments

• Python, Java (Spring Boot), and Type Script/React ecosystems
• Experience navigating both modern services and legacy application environments

• AI workflow orchestration frameworks and large language model integrations
• Production AI infrastructure, observability, evaluation tooling, and operational safeguards

• Event-driven systems and complex integration architectures
• Distributed data platforms, data pipelines, and large-scale transactional systems
• Multi-protocol integrations across APIs, messaging systems, and external vendor platforms

• Learn the platform architecture, distributed systems landscape, and engineering workflows
• Evaluate existing systems and publish architectural recommendations for modernization initiatives
• Establish engineering review and technical governance practices

• Deliver initial AI-enabled automation capabilities into production workflows
• Define scalable engineering patterns for intelligent automation and agentic systems
• Establish foundational observability, evaluation, and operational standards

• Lead key modernization and migration initiatives from design through production deployment
• Implement scalable observability and operational monitoring frameworks
• Drive platform reliability and deployment safety improvements

• Deliver additional production-scale architectural improvements and AI-enabled workflows
• Define long-term technical roadmap and scalability strategy
• Elevate engineering quality and architectural consistency across…