Note: The job is a remote job and is open to candidates in USA. LINQ is a high-growth software company focused on empowering K-12 district teams with cloud-based solutions. The Principal AI Engineer will lead the integration of AI into operational workflows, simplifying processes and creating reusable skills for various teams within the company.

Responsibilities

• Embed with teams across LINQ to learn how the work actually gets done, where the friction lives, and where agents can meaningfully simplify it. The unit of work is real workflows, not novelty demos
• Design and ship multi-step, tool-using agents that take work off people’s plates. Build with the right tools for the job — foundation model APIs, MCP servers, agent frameworks (LangGraph, OpenAI Agents SDK, Anthropic tool use patterns, or whatever fits the problem). Bias toward production-ready, not demo-ready
• Build durable, not disposable. The agent you ship to CX should leave behind reusable skills the next team can pick up, not a black box that only you understand. Think in primitives and patterns, not scripts
• Take evals and guardrails seriously. You know why a 95%-reliable agent is sometimes worse than a 70%-reliable one with a clear handoff, and you build accordingly
• Instrument what you build. Define the metrics, stand up the dashboards, and prove the impact in time saved, errors avoided, or work that no longer needs doing
• Hand it back. Train the team, document the patterns, and transition ownership to an internal champion or sustaining team so you can move to the next workflow
• First-six-month focus areas: Go-to-Market (sales motion, pipeline hygiene, RFP support) and Customer Experience (support deflection, ticket triage, knowledge management). Other functions follow
• Help define LINQ’s internal agent platform — how agents are built, evaluated, deployed, and observed. Set the patterns that the rest of LINQ’s AI work compounds against
• Establish the skills pattern — how reusable agent capabilities get packaged, versioned, evaluated, and shared across teams. This becomes the unit of leverage across LINQ
• Shape the orchestration approach — how agents discover tools, hand off to each other, escalate to humans, and remain observable when things go wrong. Use existing frameworks where they fit; build only what we genuinely need
• Evaluate and recommend AI tooling: foundation models, agent frameworks, MCP servers, automation platforms, eval and observability tooling. Make clear build-vs-buy-vs-integrate recommendations
• Co-launch an internal AI champions network alongside our Senior Director of Operations. You will own the technical substance — agent templates, reusable skills, evaluation harnesses, MCP integrations — that champions take back to their teams
• Run office hours, workshops, and enablement sessions calibrated to the audience — engineers one day, finance leaders the next
• Translate what you ship into business-relevant language. Surface success stories. Build the momentum
• Partner with the CTO on LINQ’s AI strategy and adoption roadmap; produce the proof points, case studies, and metrics that inform our board and investor narrative
• Work with Security and Compliance to ensure every agent and tool you ship meets FERPA, COPPA, PCI, and CCPA requirements. We operate in a regulated environment; this is non-negotiable
• Track the AI vendor, model, and agent-platform landscape and bring informed recommendations to LINQ’s leadership

Skills

• 8+ years building production software. The bulk of this role is shipping, and you should be comfortable doing it without a team behind you
• Direct experience building agents — multi-step, tool-using LLM workflows running in a production or near-production context. You've thought about evals, guardrails, failure modes, cost, and handoff to humans
• Working knowledge of the modern agent stack: foundation model APIs (Anthropic, OpenAI, or equivalent), at least one agent framework (LangGraph, OpenAI Agents SDK, Anthropic tool use, or similar), MCP, and evaluation tooling
• Track record of driving adoption of new tools and practices across an organization, with measurable results to show for it
• Strong fluency across the stack: scripting, light app development, API integrations, data pipelines, evaluation harnesses. You can build the thing, not just specify it
• Exceptional written and verbal communication. You can explain an eval result to a CFO and debug a tool-call chain with an engineer in the same afternoon
• Comfort with ambiguity. You will define the path, not walk one that's already paved
• Experience in a PE-backed, multi-product SaaS environment, or somewhere with comparable operational complexity
• Experience shaping an internal AI or agent platform — orchestration, skills libraries, eval pipelines,