Senior Software Developer, Applied AI New CANADA

Join our mission to provide governments with exceptional experiences so they can do the same for their communities

What do we do? We empower governments to deliver exceptional citizen experiences.

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About Clariti 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.

About the role Engineering at Clariti runs on our Agentic SDLC framework: agent specs, reusable prompts, orchestration templates, and eval harnesses that let small pods deliver at multiples of traditional velocity. 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.

What you will do:

Build and evolve the Agentic SDLC framework.

Design and implement the agent workflows, orchestration templates, and reusable components the build pods run on. Harden what exists, extend what is missing, and keep the framework fast as model capabilities and our delivery patterns change.

Build the connector layer.

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.

Make evals the backbone.

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. If we cannot measure it, we cannot scale it.

Own AI observability and cost telemetry.

Instrument token spend, latency, eval pass rates, and usage across every production agent workflow. Build the dashboards and alerts that turn "AI is expensive and mysterious" into a managed system with unit economics per workflow.

Engineer the guardrails.

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 safe 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.

What you bring

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. Distributed systems, API design, CI/CD, and cloud infrastructure are home territory.

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.

A platform temperament: you measure your success by other teams' throughput, you write documentation people actually use, and you would rather delete code than defend it.

Comfort operating with a small blast radius and high autonomy: this is a pod of few with a company‑wide mandate, not a large team with narrow lanes.

Nice to have

Experience in regulated or public‑sector software, where audita