Senior Software Developer (Applied AI)

Clariti; Canada; On-site; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Flexible Work Culture

Professional Development

Team Building Events/Outings

Motivated Team

Corporate Bonus Plan

Job summary

Clariti is seeking a senior software engineer to own the AI infrastructure on our pod, acting as DevOps for AI. You design and evolve the Agentic SDLC framework, build the connector layer, eval harnesses, observability, and guardrails that scale for government-grade usage.

You will work with a Technical Product Manager and lead architecture decisions across pipelines, cost telemetry, and security, ensuring fast, reliable delivery of AI-enabled workflows in a small, autonomous team.

Qualifications

• 6+ years as a software engineer shipping production systems.
• Experience building or operating LLM-powered or agentic systems used by real users.
• Background in regulated or public-sector software where auditability matters.

Responsibilities

• Build and evolve the Agentic SDLC framework, including agent workflows and orchestration templates.
• Design, implement, and operate the connector layer and MCP servers for core system integrations.
• Maintain eval harnesses with regression suites and CI gates to measure impact of model changes.
• Own AI observability and cost telemetry with dashboards and alerts for production workflows.
• Engineer guardrails with permissions, audit trails, and output controls for government-grade defensibility.
• Ship enablement infrastructure with reusable libraries, templates, and self-serve tooling.

Skills

Distributed systems

API design

CI/CD

Cloud infrastructure

LLM-powered / agentic systems

Observability & telemetry

Security & governance

Tools

Claude Code

LangGraph

MCP

Job description

• 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
• 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
• How success is measured:
• 90 days: 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. You have taken over operational ownership of the existing connectors and eval harnesses from the founding team, and your first improvement to the framework, scoped with the Pod Lead against what the pods actually need, is live
• 12 months: 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 connector layer covers our core systems and runs like production infrastructure. The Clariti AI harness serves a majority of non-engineering staff weekly, with uptime and support load a pod of few can sustain

Benefits

• Flexible Work Culture
• Professional Development
• Team Building Events/Outings
• Motivated Team
• Corporate Bonus Plan

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 dataA platform temperament: you measure your success by other teams’ throughput, you write documentation people actually use, and you would rather delete code than defend itComfort 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 lanesStrong 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 territory6+ 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 prototypesExperience in regulated or public-sector software, where auditability and defensibility of outputs matterPrior DevOps, platform engineering, or internal developer platform ownership; you have lived the difference between building a tool and driving its adoptionExperience instrumenting and optimizing LLM cost and quality at scale: model routing, caching, prompt compression, fine-tuning trade-offs