Job Overview:
The Senior Engineer, Agent Integration & Tool Platform is responsible for designing and operating the enterprise platform that enables secure, governed, and reusable integration between AI agents, enterprise systems, APIs, applications, data products, and external services.
This role will establish the standards, frameworks, and platform capabilities that allow development teams to rapidly onboard tools and services while maintaining enterprise requirements for security, governance, resiliency, observability, and auditability.
The ideal candidate combines deep expertise in distributed systems, API architectures, platform engineering, and emerging agent interoperability standards to create a scalable foundation for enterprise AI ecosystems.
Responsibilities
Agent Integration Platform
Design and implement enterprise capabilities that enable AI agents to securely interact with enterprise systems, APIs, applications, and services.
Establish reusable integration patterns that accelerate adoption while reducing implementation complexity for development teams.
Define enterprise standards for agent interoperability and service integration.
Tool Registry & Discovery Services
Build and operate centralized capabilities for tool registration, discovery, metadata management, and lifecycle governance.
Establish standards for tool contracts, schemas, versioning, ownership, and operational support.
Develop mechanisms that enable agents and applications to discover and consume approved enterprise capabilities.
Agent-to-Agent Communication
Define patterns and standards for communication and collaboration between autonomous and semi-autonomous systems.
Design mechanisms for agent capability discovery, delegation, orchestration, and coordination.
Establish governance and observability frameworks for multi-agent environments.
Platform Governance & Controls
Implement controls supporting authentication, authorization, entitlement management, and policy enforcement.
Partner with Information Security, Risk, Compliance, and Enterprise Architecture teams to ensure integrations comply with enterprise standards.
Ensure all platform capabilities support auditability, traceability, and regulatory requirements.
Developer Enablement
Create SDKs, templates, onboarding frameworks, and reference implementations that simplify adoption.
Establish engineering standards and best practices for tool integration and agent development.
Improve developer productivity through automation, self-service capabilities, and platform abstractions.
Platform Operations & Observability
Build capabilities supporting monitoring, tracing, usage analytics, performance management, and operational governance.
Establish metrics for tool adoption, reliability, utilization, and business impact.
Ensure platform resiliency, scalability, and operational excellence.
Technical Leadership
Lead architecture reviews and engineering design decisions.
Mentor engineers and influence technical direction across multiple teams.
Drive engineering excellence through best practices, reusable patterns, and platform modernization initiatives.
Success Measures
Reduction in time required to onboard new tools and enterprise capabilities.
Increased reuse of enterprise services across applications and AI systems.
Consistent implementation of governance and security controls across integrations.
High platform reliability, observability, and operational maturity.
Accelerated adoption of agent-based capabilities across the enterprise.
Requirements:
Minimum of 8 years of software engineering experience.
Experience designing and building enterprise integration platforms, API platforms, or distributed systems.
Knowledge of API architecture, service contracts, event-driven systems, and platform engineering principles.
Experience implementing authentication, authorization, and governance controls in enterprise environments.
Experience building developer platforms, SDKs, shared services, or integration frameworks.
Core Competencies:
Designs and enables AI agent-to-tool communication frameworks, including tool discovery, service orchestration, and integration patterns that allow agents to securely access APIs, data sources, and enterprise services at scale.
Understanding of cloud-native architectures and modern software engineering practices.
Preferences:
Experience with agent frameworks, agent orchestration, or autonomous systems
Experience with Model Context Protocol (MCP), tool registries, service catalogs, or capability management platforms.
Experience with enterprise API management, service mesh, or integration platforms.
Familiarity with AI and machine learning platforms and their integration requirements.
Experience operating in highly regulated environments.
Pay Range:
$115,154.00 - $191,889.00
Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play – such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer!
