AI Operations Engineer

Turtle and Hughes•Clark, NJ

18h•$100,000 - $125,000•Remote

About The Position

The AI Operations Engineer is a hands-on, technical role responsible for building and supporting Turtle's day-to-day AI operations. The role develops and maintains AI agents and chatbots, migrates individually created or shadow AI tools into enterprise-managed and access-controlled equivalents, and keeps the underlying platforms, integrations, and cost structures healthy. The position works closely with the AI Center of Excellence, Data Services and Enterprise Architecture, and Cyber Security to deliver AI capabilities that are supportable, secure, and cost-effective. This position is remote, with periodic travel to Turtle's Clark, NJ office for team meetings and stakeholder collaboration.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent hands-on experience.
3 to 5 years of experience in an AI, automation, integration, or DevOps/AIOps engineering role.
Practical experience building or supporting AI agents or chatbots in a business environment.
Working knowledge of REST APIs and experience building or maintaining API integrations.
Strong troubleshooting skills across applications, integrations, and infrastructure.
Ability to work independently while collaborating across IT, business, and security teams.

Nice To Haves

Familiarity with NeuralSeek, Claude, Microsoft Copilot, or comparable AI orchestration and assistant platforms.
Experience with Data Dog or similar observability and monitoring platforms.
Exposure to Model Context Protocol (MCP) or similar agent-to-system integration frameworks.
Understanding of cloud or SaaS cost management (FinOps) principles as applied to AI consumption.
Experience operating in a regulated or compliance-driven environment (for example, ISO 27001 or NIST CMMC).
Relevant certifications (for example, in AI platforms, cloud services, or FinOps) are beneficial but not required.

Responsibilities

Build, test, and support AI agents and chatbots that address internal business use cases, translating stakeholder requirements into working designs and improving them based on feedback and usage data.
Maintain and continuously improve AI agents and chatbots in production, including prompt and workflow tuning, error handling, and performance monitoring.
Identify individually created or informally deployed AI agents, bots, and chat tools across the organization and migrate them to enterprise-managed, centrally hosted, and access-controlled platforms.
Apply consistent governance, authentication, and access control standards to migrated tools, in coordination with Cyber Security and the AI Center of Excellence.
Administer and support core AI platforms, including NeuralSeek, Claude, and Copilot, and monitor platform health, performance, and usage using Data Dog and related observability tools.
Evaluate new AI tooling and platform capabilities and recommend adoption where appropriate.
Support and maintain Model Context Protocol (MCP) server infrastructure that connects AI agents to enterprise data and systems.
Build, test, and maintain API integrations linking AI agents and chatbots to enterprise applications and data sources, and resolve integration issues with internal teams and vendors.
Monitor and manage consumption-based AI costs, including token and API usage, and support cost allocation, budgeting, forecasting, and optimization without compromising performance.
Maintain an up-to-date inventory of AI agents, bots, and integrations in production, including ownership, data access, and business purpose.
Participate in incident response for AI-related issues, including outages, integration failures, and unexpected model behavior.
Support vendor management for AI platform providers, including renewals, license reconciliation, and issue escalation.
Develop and deliver training and enablement materials so business users adopt enterprise AI tools instead of building their own.
Ensure agents and integrations comply with Turtle's AI governance, data handling, and security policies.
Document architecture, configurations, and runbooks for supported agents, chatbots, and integrations to reduce key-person dependency.