# AI Engineer at Arkion Identity Systems in Toronto, Canada… > Source: https://confidential.careers/job-detail/ai-engineer-arkion-identity-systems-toronto # AI Engineer Arkion Identity Systems ** Toronto, Canada ** Full Time ** Engineering Jobs ** Canada ** Posted 6d ago [Apply Now](https://confidential.careers/go/13487139) ** Save ## Job Description ### Risk Estimator ARKION Platform Standard For Executives Built for boards, CIOs, and audit committees ### Executive Brief A non-technical overview of the NHI governance gap and how Arkion closes it. Built to forward to your board. ### Compliance Crosswalk DORA, NIS2, ISO 27001:2022, SEC Cyber-Disclosure — mapped to exactly which Arkion capability satisfies each control. ### Cost of Inaction Translate NHI exposure into dollars. The cost of a credential-related breach measured against the cost of governing one. ### Trust Center· soon Security architecture, sub-processors, DPA / MSA / BAA templates, attestation roadmap. Built for procurement. ### The NHIG Standard Version 1.0 of the Non-Human Identity Governance Standard. Principles, vocabulary, maturity model. Open for comment. ### Field Notes Regulatory updates, principle additions to the Standard, breach post-mortems. Slow, considered writing from inside the category. ### AI Agent Governance What it means to govern an AI agent's identity — provisioning, scoping, revoking. Topic explainer. ### Certificate Lifecycle From issuance through rotation to revocation. The cryptographic primitives Arkion is built on. ### Identity Registry The single system of record for every non-human identity in your enterprise. ### Why Now DORA. NIS2. SEC Cyber-Disclosure. The 2024–2026 timeline that turned NHI governance from optional to required. ### Risk Estimator Two minutes. Seven questions. A directional estimate of how many non-human identities are operating outside any governance boundary. ### Discovery Scan A read-only scan of one environment. Every NHI found, named, scored. Delivered to your inbox. No agents installed. ### Implementation Timeline Day 1 scan. Week 1 findings call. Week 2–3 pilot. Week 4 governed estate. The path from first call to first audit answer. ### Engineering ### AI Engineer (Developer Productivity) Location: Hybrid Greater Toronto Area Type: Full-Time Reports to: Chief Product Officer ### About the Role We are looking for an AI Engineer who can design and build production-grade AI systems to accelerate software development, testing, and deployment across our platform. This role focuses on LLM-powered agents, orchestration frameworks, and intelligent automation, not just experimentation. You will build systems that are stateful, scalable, secure, and integrated into real engineering workflows. You will work closely with the Software Architect, Rust Backend Engineers, Senior Frontend Engineer, and Cloud & Deployment Engineer to embed AI into both: our product (NHI / security platform), and our internal engineering stack. ### Responsibilities What you’ll own. ### AI agents & orchestration - Design and implement AI agents capable of code generation, review, and refactoring. - Build agents for test generation and validation. - Build agents for deployment automation and troubleshooting. - Build agents for documentation generation and knowledge retrieval. - Build multi-step, stateful workflows using tool calling, task planning, and execution graphs. ### LLM systems, SDKs & memory management - Build systems using OpenAI SDK, Anthropic SDK, and AWS AgentCore. - Design and implement memory strategies: short-term (context window), long-term (vector DB / retrieval), and session-based memory for agents. - Use frameworks such as LangGraph, LangChain / LlamaIndex (or similar). - Implement Retrieval-Augmented Generation (RAG), tool/function calling, and multi-agent coordination. ### Developer productivity & automation - Build tools that assist engineers working in Rust, Next.js, and cloud-native systems. - Generate boilerplate code, tests, and API integrations. - Improve debugging and observability workflows. - Integrate AI into Git workflows (PRs, reviews, commits), CI/CD pipelines, and internal developer tools. ### Cloud & deployment integration - Deploy AI systems in Kubernetes environments and containerized systems (Docker). - Build AI-driven systems for deployment validation, incident analysis, and cloud cost optimization. - Ensure reliability, scalability, and observability of AI pipelines. ### Required Skills What you’ll bring day one. ### Core - Strong experience designing and shipping production-grade AI systems (not just experimentation). - Hands‑on experience with LLM SDKs (OpenAI, Anthropic) and orchestration frameworks (LangGraph, LangChain, LlamaIndex, or equivalent). - Experience implementing RAG, tool/function calling, and multi‑agent coordination. - Familiarity with vector databases and short/long‑term memory strategies. - Comfortable working across engineering, product, and infrastructure. - Experience deploying AI services in containerized / Kubernetes environments. ### Who You Are - You think in systems, workflows, and automation, not just models. - You focus on real-world impact and production readiness. - You are comfortable working across engineering, product, and infrastructure. - You enjoy building tools that other engineers rely on daily. - You thrive in high-ownership, fast-moving environments. ### Why Join Us - 01 Introduce AI-first workflows across engineering and deployment. - 02 Improve developer velocity and product quality. - 03 Reduce manual effort across teams. - 04 Help build a modern, AI-driven engineering platform. ### Company - About - Team - Careers - Pricing - Contact ### Trust & Legal - Trust Center - Security - Privacy - Terms ### ARKION © 2026 Arkion Identity Systems, Inc. #J-18808-Ljbffr Posted August 10, 2026