Senior Software Developer, Applied AI

CanadaRemoteFull-timesenior

OtherSoftware Developer

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Quick Summary

Requirements Summary

6+ years of professional software engineering experience building and shipping production systems.

Technical Tools

OtherSoftware Developer

The Senior Software Developer, Applied AI will build the infrastructure that enables teams to develop and use AI-powered workflows at scale.
Rather than shipping traditional product features, you will focus on the platforms, frameworks, connectors, and guardrails that make AI-assisted work faster and more reliable.
You will play a senior engineering role within a small, highly autonomous Applied AI team with company-wide impact.
The position combines agentic systems, distributed systems, API design, cloud infrastructure, evaluation frameworks, and AI observability.
You will work closely with a Technical Product Manager while taking significant ownership of architecture, implementation, and technical direction.
Your work will help both engineering and non-technical teams safely apply AI to real operational workflows, including in a public-sector software environment.

Design, build, and evolve an Agentic Software Development Lifecycle framework, including agent workflows, orchestration templates, and reusable components.
Strengthen and extend existing AI infrastructure while ensuring the framework remains reliable and adaptable as models and delivery practices evolve.
Build and operate the connector layer, including MCP servers and integrations with core systems, with strong permissions, versioning, testing, and monitoring.
Develop evaluation harnesses, regression suites, automated quality gates, and scoring infrastructure to measure and continuously improve agent performance.
Instrument AI workflows to track token consumption, latency, evaluation pass rates, usage, and other operational metrics.
Build dashboards and alerts that provide visibility into AI quality, performance, reliability, and workflow-level economics.
Implement guardrails covering permissions, audit trails, version control, and output controls to support trustworthy AI-assisted workflows in regulated and government-oriented environments.
Develop skill and template libraries, onboarding experiences, and self-service tools that enable non-technical employees to use AI effectively.
Establish feedback loops that capture usage data and insights to inform platform improvements and future AI initiatives.
Take operational ownership of connectors, evaluation systems, and other existing Applied AI infrastructure.
Partner with the Technical Product Manager and other stakeholders to determine technical priorities and translate roadmap requirements into scalable implementations.
Measure platform success through improvements in reliability, adoption, developer productivity, AI quality, and operational efficiency.

Requirements

~1 min read

6+ years of professional software engineering experience building and shipping production systems.
1–2+ years of hands-on experience building LLM-powered or agentic systems used by real users in production environments.
Strong software engineering fundamentals, including distributed systems, API design, CI/CD, and cloud infrastructure.
Practical experience with agent frameworks and coding agents such as Claude Code, LangGraph, or equivalent technologies.
Experience with MCP or comparable tool protocols, structured outputs, and evaluation-driven development.
Strong understanding of context management and the ability to make technical decisions based on measurable results and data.
Platform engineering mindset, with an emphasis on enabling other teams to work more effectively and building systems that people can successfully adopt.
Ability to document systems clearly and develop reusable infrastructure and tooling.
Strong autonomy and ownership, with the ability to operate effectively within a small team with a broad organizational mandate.
Excellent communication and collaboration skills, particularly when working across technical and non-technical teams.
Experience in regulated or public-sector software environments is a plus.
Prior DevOps, platform engineering, or internal developer platform ownership is desirable.
Experience optimizing LLM costs and quality at scale, including model routing, caching, prompt compression, or fine-tuning trade-offs, is a plus.