Senior Platform Engineer - AI Enablement

Acuity Inc.•Boulder, CO

1d•$120,800 - $261,000•Hybrid

About The Position

This team exists to make AIS’ engineers and product managers dramatically more productive with AI — it does not build the AI-powered features that ship inside AIS’ products (Atrius, Distech, and QSC already own that). Think of us as the internal platform/enablement group: we build the tools, agents, and standards that let the rest of product engineering move faster and safer with AI, and we own the metrics that prove it's working. We're deliberately scoped as a tools and enablement team, not a research group or an organizational-transformation function — we make transformation possible for others rather than driving it ourselves. You'll be working shoulder-to-shoulder with a small, senior group to stand up the platform, tooling, and practices that the rest of engineering will rely on. This role blends hands-on software/platform engineering with a "developer relations" mindset — you're as motivated by making other engineers more effective as you are by building things yourself.

Requirements

Strong, well-rounded software engineering background — this is a generalist "platform engineer" or “DevOps” or "DevEx"-minded role, not a role for someone who identifies purely as an "AI engineer." This role is about enablement as much as it is about AI.
Hands-on experience with agentic development — you've built and orchestrated AI agents (single or multi-agent systems), not just called an LLM API from a script.
Experience with AI evaluation and benchmarking and prompt engineering.
Working knowledge of AI security and governance concepts for internal tooling (e.g., prompt injection risk, guarding against cost/behavior regressions in agent pipelines).
A track record of completing large efforts (multi-month) largely independently — this role requires figuring out what needs to be solved, not just executing a detailed task list.
Strong judgment around platform abstractions: knowing when to build a reusable, standardized path versus when to stay flexible for a one-off need.
Strong collaboration and communication skills — you'll regularly work with Security, IT, and non-technical stakeholders, and this is weighted as heavily in our evaluation as technical depth.
Based in, or able to reliably commute to the Denver, CO metro area.

Nice To Haves

Experience running training or enablement programs for engineering tools.
Experience building developer/internal platforms used by multiple teams (a "golden path" or self-service mindset).
Familiarity with agent-orchestration frameworks (e.g., Claude SDK, LangGraph, or similar).
Experience with engineering telemetry, observability, or CI/CD pipelines.
Experience with AI-native coding tools (e.g., Claude Code, Cursor) and opinions on how they fit into a real engineering workflow.

Responsibilities

Build and maintain internal AI productivity tools and the platform used to develop, package, and distribute them across engineering and product development.
Design, build, and operate internal AI agents and automations — whether standalone (e.g., agents running on a web service) or embedded (e.g., a skill invoked from a chat or IDE surface). We treat these as variations on the same underlying platform, and you may end up shipping the same capability in both forms.
Define productivity metrics for engineering/product-development work and evaluate our tools and agents against them, iterating based on results.
Own AI governance for internal tooling: guardrails and review processes (e.g., preventing token-cost regressions, bad context/prompt injection reaching an agent), access controls, and standards for what "safe to deploy" means for internal AI tools and agents.
Partner with Security and IT to clear the path for AI tooling adoption — you'll need real influencing and communication skills to work through the friction that comes with that territory.
Manage identity and access for AI agents distinctly from human users — as agents get real tool/system access, their credentials, permissions, and audit trail need the same rigor as a human account, if not more.
Lead training and enablement sessions to help engineers and PMs get real value from the tools you build.
Treat adoption as a first-class engineering problem — track measurable outcomes (tool adoption rates, time saved, reduction in manual toil) and iterate the platform based on what actually gets used, not just what gets shipped.
Collaborate with the teams building AI-powered product features (Atrius, QSC, etc.) as a supporting/accelerating partner, without owning their product roadmap.