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AI Platform Engineer

  • Toronto, ON, Canada

Salary Range: CAD$ 90,000 - 100,000 · Full-time

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Job details

Pay
Salary Range: CAD$ 90,000 - 100,000
Job type
Full-time
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Posting language
English
Posted

Original job description

About the Job: We’re looking for a hands-on AI Platform Engineer to build and operate production-ready LLM agent systems and anomaly detection tools. You’ll own solutions from design through deployment and monitoring, apply practical AI governance, and identify opportunities to improve how engineering and operations teams work.

Office Location: Toronto

Employment Type: Permanent

Role Type: New position – current requirement

Work Arrangement: Hybrid (2 days in office)

Position Responsibilities:

  • Design, build, and operate LLM agent systems using Google ADK or a comparable framework such as LangGraph, CrewAI, AutoGen, or OpenAI Agents SDK.
  • Own delivery end to end, including architecture, coding, containerization, CI/CD, deployment to Kubernetes or cloud infrastructure, and post-launch monitoring.
  • Build agent tools as MCP servers and integrate with REST APIs.
  • Address production concerns such as tool timeouts, oversized responses, retries, and token costs.
  • Develop or improve time-series forecasting and anomaly detection using production metrics, such as Prometheus or Grafana data.
  • Tune detection thresholds against real incidents and balance false positives against missed alerts.
  • Use AI coding tools such as Claude Code, Cursor, or Copilot regularly as part of daily engineering work.
  • Extend AI coding tools for team use through plugins, skills, slash commands, hooks, or MCP integrations.
  • Review and verify AI-generated code before it ships.
  • Apply safeguards to AI systems, including code-enforced access controls, PII and secrets handling, audit logging, cost limits, and model output evaluation.
  • Work with security and compliance teams through internal review processes.
  • Identify opportunities by observing how engineering and operations teams work, then prototype and deliver tools that people adopt.



Requirements

Required Qualifications:

  • Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
  • 4+ years of experience shipping LLM agent system to production within a company.
  • End-to-end experience with a production agent system, including architecture, implementation, deployment through CI/CD to Kubernetes or cloud, and monitoring after launch.
  • Experience building agent tools as MCP servers and integrating with REST APIs.
  • Understanding of production issues such as tool timeouts, oversized responses, retries, and token cost management.
  • Hands-on experience with time-series forecasting or anomaly detection on real production metrics.
  • Experience tuning alert thresholds against real incidents and managing the trade-off between false positives and missed alerts.
  • Regular use of AI coding tools as part of daily engineering work, with a disciplined approach to reviewing and validating generated code.
  • Practical experience making AI systems safe for enterprise use, including access controls enforced in code, PII and secrets handling, audit logging, cost limits, and model evaluation.
  • Comfort working within security and compliance review processes.

Preferred Qualifications:

  • Experience extending AI coding tools for a team through custom plugins, skills, slash commands, hooks, or MCP integrations.
  • Experience deploying and monitoring containerized services on Kubernetes or a cloud platform.
  • Strong software engineering practices, including testing, observability, CI/CD, and production support.
  • Ability to explain technical decisions and operational trade-offs to engineering, operations, and security stakeholders.



Benefits

Salary Range: CAD$ 90,000 - 100,000

The final compensation offered will depend on local market conditions and geographic location, as well as job-related factors such as the candidate’s knowledge, skills, qualifications, relevant experience, and education/training. Compensation may also include additional components such as benefits, and/or other incentives, where applicable. In accordance with new employment standards requirements, we retain copies of this job posting and applicant information for three (3) years after the posting is removed. We do not use AI technology; all applications are also reviewed by our recruitment team.

More about this job

Responsibilities

Design, build, deploy, and monitor production LLM agent systems, including MCP-based tools and REST API integrations, while managing reliability, performance, and token costs. Develop and tune anomaly detection and forecasting solutions, extend AI coding tools for team use, and apply enterprise safeguards in collaboration with security and compliance teams.

Requirements

Requires a bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience, and at least four years of experience shipping LLM agent systems to production. Candidates should have end-to-end production deployment and monitoring experience, hands-on anomaly detection or forecasting experience, disciplined AI-generated code review practices, and practical experience with enterprise AI safeguards.

Skills

  • LLM Agent Systems
  • Google ADK
  • LangGraph
  • MCP Servers
  • REST APIs
  • Kubernetes
  • CI/CD
  • Time-Series Forecasting
  • Anomaly Detection
  • Prometheus
  • Grafana
  • AI Coding Tools
  • AI Governance
  • Access Controls
  • Audit Logging
  • Model Evaluation

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Engineering
  • Data & Analytics
  • Retail

Keywords

  • LLM Agent Systems
  • Google ADK
  • LangGraph
  • CrewAI
  • AutoGen
  • OpenAI Agents SDK
  • MCP Servers
  • REST APIs
  • Kubernetes
  • Cloud Infrastructure
  • Containerization
  • CI/CD
  • Production Monitoring
  • Time-Series Forecasting
  • Anomaly Detection
  • Prometheus
  • Grafana
  • Claude Code
  • Cursor
  • GitHub Copilot
  • AI Governance
  • Access Controls
  • PII Handling
  • Secrets Management
  • Audit Logging
  • Cost Limits
  • Model Evaluation
  • Security Reviews
  • Compliance
  • Alert Threshold Tuning

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