Senior Enterprise AI Control Plane Engineer

Pilot Thomas Logistics•Boston, MA

4d•Onsite

About The Position

Our world is transforming, and PTC is leading the way. Our software brings the physical and digital worlds together, enabling companies to improve operations, create better products, and empower people in all aspects of their business. Our people make all the difference in our success. Today, we are a global team of nearly 7,000 and our main objective is to create opportunities for our team members to explore, learn, and grow – all while seeing their ideas come to life and celebrating the differences that make us who we are and the work we do possible. We are looking for a Senior Enterprise AI Control Plane Engineer with 5 to 8 years of experience to help set up, manage, and operate our enterprise AI governance and integration layer. This role will focus on configuring and managing MCP Gateways, AI/LLM Gateways, policies, access controls, governance, observability, and agent registries. The ideal candidate is techno-functional, with a strong foundation in API Management / API Governance, and an interest in extending those concepts into the emerging AI agent and MCP ecosystem. The person in this role will work closely with business stakeholders, architecture, security, platform teams, and application owners as teams build AI agents that connect to internal and vendor MCP servers through a common governed gateway.

Requirements

5 to 8 years of experience in enterprise integration, API management, platform engineering, cloud engineering, or related roles.
Strong understanding of API gateways, API governance, API security, lifecycle management, policies, subscriptions, and observability.
Experience with authentication and access control patterns such as OAuth, OIDC, JWT, API keys, RBAC, service principals, managed identity, or SSO.
Ability to work with both technical and business stakeholders.
Experience supporting production platforms, troubleshooting issues, and creating operational documentation.
Strong communication skills and ability to explain governance and platform concepts clearly.
Applicants must be authorized to work in the United States without requiring current or future sponsorship from PTC.

Nice To Haves

Experience with one or more of the following platforms or equivalent technologies: LiteLLM or other LLM gateway platforms for model routing, budgets, guardrails, logging, and usage tracking
Azure API Management MCP capabilities for exposing APIs as MCP servers and governing MCP traffic.
Microsoft Foundry AI Gateway for centralized governance of MCP traffic, access control, rate limits, and audit logging.
Microsoft Agent 365 Registry for centralized visibility and governance of enterprise AI agents.
Salesforce Agentforce / MuleSoft Agent Fabric / Omni Gateway for agent discovery, orchestration, governance, and observability.
Boomi API Control Plane / Boomi MCP capabilities for exposing managed APIs as MCP tools.
Equivalent platforms such as Kong, Apigee, MuleSoft Anypoint, WSO2, Tyk, AWS API Gateway, AWS Bedrock, or other AI gateway/control plane platforms.
Model Context Protocol concepts, MCP servers, MCP clients, and MCP tools.
AI/LLM gateways, model routing, provider abstraction, usage controls, and guardrails.
API governance, API products, policies, versioning, catalogs, and lifecycle management.
Observability tools such as Azure Monitor, Application Insights, OpenTelemetry, Datadog, Splunk, Grafana, or Prometheus.
Cloud and integration platforms, preferably Azure, Boomi, MuleSoft, or equivalent.
Exposure to Claude, Codex, Cursor, or equivalent AI Engineering tools

Responsibilities

Configure and operate the Enterprise AI Control Plane, including MCP gateways, LLM gateways, policies, access control, observability, and governance.
Help onboard internal and vendor MCP servers through a common gateway for secure and observable agent access.
Define and implement governance patterns for AI agents, MCP tools, model access, rate limits, logging, approvals, and usage tracking.
Partner with business teams to understand AI agent use cases and translate them into secure platform configurations.
Collaborate with security and architecture teams on authentication, authorization, RBAC, audit logging, and policy enforcement.
Support discovery, cataloging, and lifecycle management of AI agents, MCP servers, and reusable tools
Build operational dashboards and reports for agent usage, MCP tool usage, LLM usage, errors, latency, and policy violations.
Create reusable onboarding guides, standards, runbooks, and best practices for AI agent integrations.