Join our mission to provide governments with exceptional experiences so they can do the same for their communities! We empower governments to deliver exceptional citizen experiences. Clariti builds community development software for cities and counties: permitting, licensing, plan review, and the workflows that let local governments serve their residents.

We are in the middle of an AI-native transformation. Our platform is being built by AI-native pods, and the delivery model behind it assumes a 4x to 6x reduction in implementation time versus traditional government software projects. The Applied AI pod owns that framework and is now extending it beyond engineering: into our Professional Services delivery practice and into the Clariti AI harness, the platform of connectors, skills, and guardrails that lets non-technical employees across Sales, CX, Finance, People, and PS use AI safely on real work.

You are the senior engineer on this pod.

Think of the job as DevOps for AI: you do not ship product features, you ship the infrastructure that makes everyone else faster. Where a DevOps engineer builds pipelines, golden paths, and observability for code, you build them for AI: the connector layer, the eval harnesses, the orchestration runtime, the cost and quality telemetry, and the guardrails that make agent output trustworthy enough for government software. You will work alongside a Technical Product Manager who owns the roadmap.

You own how it gets built, and much of what gets built, because on a pod this small the line between architecture and implementation is yours to draw. Design, implement, and operate MCP servers and integrations into our core systems so agents and non-technical employees can act on real company data with correct permissions.

Treat connectors as production software: versioned, tested, monitored, least-privilege by default. Build and maintain the eval harnesses that score agent output automatically: regression suites for prompts and workflows, quality gates in CI, and the scoring infrastructure that tells us whether a change to a model, prompt,



or workflow made things better or worse. Own AI observability and cost telemetry.

Build the dashboards and alerts that turn "AI is expensive and mysterious" into a managed system with unit economics per workflow. Implement the permissioning, audit trails, versioning, and output controls that let agent-assisted work stand up in a government context, where an artifact can end up in front of a planning commission. Make the secure path the default path in code, not in policy documents.

Ship enablement infrastructure. Build the skill and template libraries, onboarding flows, and self-serve tooling that take a non-technical employee from zero to producing real work with AI, and the feedback loops that route their usage data back into the platform roadmap. 6+ years as a software engineer shipping production systems, with at least 1 to 2 years building LLM-powered or agentic systems that real users depend on, not prototypes.

Strong general engineering fundamentals: you are a senior developer first and an AI specialist second. Hands‑on depth in the current agentic stack: agent frameworks and coding agents (Claude Code, LangGraph, or equivalents), MCP or comparable tool protocols, structured outputs, and eval‑driven development. You have opinions about context management and can defend them with data.

Experience in regulated or public‑sector software, where auditability and defensibility of outputs matter.

Prior

DevOps, platform engineering, or internal developer platform ownership; you have lived the difference between building a tool and driving its adoption.

Experience instrumenting and optimizing LLM cost and quality at scale: model routing, caching, prompt compression, fine‑tuning trade‑offs.



you know the Agentic SDLC framework end to end, how the build pods use it day to day, where it is strong, and where it breaks, and you are shipping to it without regression to pod velocity. the framework is measurably faster and more reliable than the day you joined, with automated eval gates on every production agent workflow and cost and quality telemetry per workflow driving routing and optimization decisions. The Clariti AI harness serves a majority of non‑engineering staff weekly, with uptime and support load a pod of few can sustain. The base salary range for this role is expected to be between $103,000- $160,000 based on the candidate’s skills, experience, and qualifications while considering internal pay equity and our broader pay philosophy.

Benefits depend on employment type (full‑time, part‑time, contract, etc). Because our customers trust us with sensitive information, we require all successful candidates to undergo comprehensive background checks before joining our team. Travel - Although we operate as a remote company, all roles are expected to participate in occasional travel for in‑person company‑wide or departmental meetings, typically 1-2 times per year. Additional travel requirements specific to the role, if any, will be outlined in the . We're committed to building an inclusive culture where our team members over projects, tasks, and outcomes; customer‑focused by keeping the customer at the heart of decision‑making. It’s the diversity of our team that helps us make better decisions, by leveraging the diversity in thought &

experience across to create impactful solutions as we explore new paths & challenges as we grow. We’re working to create a workplace and team that is as diverse as the communities we serve. We are here to help

If you require accommodations in completing an application, interviewing, completing any pre‑employment testing, or otherwise participating in our hiring process for any reason, please direct your questions to hr@claritisoftware.

📌 Senior Software Developer, Applied AI (Ottawa)
🏢 Clariti Cloud
📍 Ottawa