Principal Engineer, Agentic AI Platform Integrations

Vertex Inc.•Boston, MA

1d•$166,640 - $250,000•Hybrid

About The Position

Vertex is seeking an Integrations Engineer, Agentic AI Platform to connect the AI/agentic platform to the systems, tools, and data sources that make AI valuable in real enterprise workflows. This role will focus on designing and implementing secure, scalable, and reliable integrations across internal platforms, third-party applications, and business-critical services. The ideal candidate brings strong expertise in MCP/A2A protocols, APIs, event-driven architectures, authentication patterns, and enterprise integration design. This individual will play a key role in ensuring AI capabilities are embedded into day-to-day work at Vertex and not as isolated proofs of concept, but as production-ready solutions integrated into the enterprise ecosystem.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or a related technical field; equivalent practical experience may be considered
8+ years of experience in software engineering, platform engineering, or enterprise integrations
Strong hands-on experience designing and implementing API-based integrations in enterprise environments
Experience with MCP, A2A, service-to-service communication, or similar integration/interoperability patterns
Strong understanding of event-driven architecture, messaging systems, asynchronous processing, and workflow-based integration design
Experience with authentication and authorization standards such as OAuth, OIDC, SSO, service principals, secrets management, and token-based security
Familiarity with enterprise application integration patterns, middleware, and distributed systems design
Experience building secure, scalable, and reliable production services
Strong problem-solving skills and ability to work across multiple systems, teams, and technical domains
Strong written and verbal communication skills with the ability to document and explain integration approaches clearly
Deep understanding of AI-native software engineering practices and experience establishing standards, governance, and best practices for the responsible use of AI coding assistants and software engineering agents across engineering teams
Experience defining architecture, standards, and reusable services for large-scale enterprise environments
MCP and A2A protocols
REST APIs and service integration
Webhooks and event-driven integration patterns
Enterprise application integration
Authentication and authorization design
OAuth, OIDC, SSO, and token management
Distributed systems and microservices
Messaging and asynchronous processing
Integration observability, logging, and monitoring
Data exchange patterns across enterprise systems
Secure connectivity and secrets management
Cloud platforms and cloud-native integration architectures
Error handling, resiliency, and performance optimization
Versioning, testing, and deployment of integration services

Nice To Haves

Experience integrating AI or agentic platforms with enterprise tools and business systems
Familiarity with workflow orchestration platforms and automation frameworks
Experience building reusable connectors or SDKs for platform extensibility
Knowledge of enterprise data platforms, document systems, knowledge repositories, and collaboration tools
Experience with regulated environments such as biotechnology, pharmaceuticals, healthcare, or life sciences
Exposure to AI platform patterns including tool use, agent execution, retrieval, and action frameworks
Experience supporting platform governance, security reviews, and integration documentation
Understanding of observability practices for complex, multi-system workflows

Responsibilities

Design, build, and maintain integrations between the AI/agentic platform and enterprise systems, internal applications, third-party tools, and business services
Implement secure, scalable connectivity patterns that enable AI agents and workflows to access data, trigger actions, and interact with enterprise platforms
Develop and support integrations using MCP, A2A, APIs, webhooks, and event-driven architectures
Create reusable connectors, middleware components, and service patterns that accelerate onboarding of new tools and systems into the AI platform
Partner with platform, product, data, security, and enterprise application teams to ensure integrations meet technical, business, and compliance requirements
Design robust authentication and authorization patterns for integrated AI services, including identity propagation, token handling, and access control
Enable AI workflows to interact reliably with core enterprise platforms such as collaboration tools, knowledge systems, workflow tools, and line-of-business applications
Support integration patterns for structured and unstructured data sources used by AI and agentic applications
Implement monitoring, logging, tracing, error handling, and alerting for integration services and platform connectors
Ensure integrations are resilient, maintainable, observable, and aligned with enterprise architecture standards
Contribute to platform governance by defining integration standards, documentation, onboarding guidance, and best practices
Troubleshoot and resolve issues related to connectivity, data exchange, authentication, latency, and downstream service dependencies
Evaluate emerging standards and tools in AI system interoperability and enterprise integrations to inform platform evolution