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Arkion Identity Systems

AI Engineer

Toronto, ON, Canada
Greater Toronto Area, Canada, Canada

Job type: Full-time
Apply by: Oct 2, 2026
Posting language: English
Posted: Aug 5, 2026

Apply About employer

Applications open in a new tab.

Original job description

ARKION PlatformStandard

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.

Learn

Field notes, principles, and the Standard

The NHIG Standard

Version 1.1 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.

Tools

Estimate, scan, and act

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.

Browse the full libraryAll resources→

PricingTeam

We’re hiring·4 roles→Free ScanScan→

PlatformStandardResourcesPricingTeamRequest Discovery Scan→We’re hiring· 4 roles →

← All open roles

Engineering
AI Platform

AI Engineer (Developer Productivity)

LocationHybrid

Greater Toronto Area

TypeFull-Time

Reports toChief Product Officer

Apply for this role→

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.

Ready to apply?

Email your application directly to FR03@arkion.ai. Include the following so we can move quickly:

Resume
GitHub or portfolio (AI or automation projects)
Examples of AI agents, workflow orchestration systems, or developer productivity tools

Apply by email→← All open roles

ARKION

The governance layer for non-human identity. Purpose-built for the machine estate beneath the enterprise: AI agents, service accounts, and the cryptographic substrate underneath them.

Product

Platform
Capabilities
Discovery Scan

For Executives

Executive Brief
Compliance
Cost of Inaction
Risk Estimator

Learn

NHIG Guide
The NHIG Standard
Vocabulary
Field Notes
AI Agent Governance
Certificate Lifecycle
Identity Registry

Company

About
Team
Careers
Pricing
Contact

Trust & Legal

Trust Center
Security
Privacy
Terms

Apply About employer

More about this job

Responsibilities

Design and deploy production-grade AI agents and stateful workflows for code generation, testing, review, deployment, troubleshooting, and documentation. Integrate AI into developer tools, Git workflows, CI/CD, and cloud environments, ensuring systems are reliable, scalable, and observable.

Requirements

The role requires experience shipping production AI systems, using LLM SDKs and orchestration frameworks, and implementing RAG, tool calling, multi-agent coordination, and memory strategies. Candidates should also have experience deploying AI services in containers or Kubernetes and be comfortable working across engineering, product, and infrastructure.

Skills

Production-Grade AI Systems
LLM SDKs
AI Agent Orchestration
LangGraph
LangChain
LlamaIndex
Retrieval-Augmented Generation
Tool and Function Calling
Multi-Agent Coordination
Vector Databases
Memory Management
Rust
Next.js
Kubernetes
Docker
CI/CD

Visa sponsorship

Not detected in the job text

Keywords

Artificial Intelligence
Large Language Models
OpenAI SDK
Anthropic SDK
AWS AgentCore
LangGraph
LangChain
LlamaIndex
Retrieval-Augmented Generation
Tool Calling
Function Calling
Multi-Agent Coordination
Vector Databases
Agent Memory
Code Generation
Test Generation
Rust
Next.js
Kubernetes
Docker
Git Workflows
CI/CD
Deployment Automation
Observability
Cloud Cost Optimization
Developer Productivity
Security Platform
Stateful Workflows

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