AI Platform Engineer

Infoya Inc.; Toronto; Hybrid; CAD 90,000 - 100,000; Full time

Job summary

Infoya is seeking a hands-on AIPlatform Engineer to build and operate production-ready LLM agent systems and anomaly detection tools. You’ll own solutions from design through deployment and monitoring, applying practical AI governance and driving AI efficiency across engineering and operations.

The role is based in Toronto with a hybrid work arrangement (2 days in office). Permanent employment with opportunities to influence security, tooling, and scalable AI initiatives.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
• 4+ years of experience shipping LLM agent system to production.
• 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.

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.

Skills

LLM agents

AI governance

production mindset

security awareness

Education

Bachelor’s degree in Computer Science or Engineering

Tools

Google ADK

LangGraph

CrewAI

AutoGen

OpenAI Agents SDK

Kubernetes

CI/CD

REST APIs

Prometheus

Grafana

Claude Code

Cursor

Copilot

MCP servers

Plugins / hooks / slash commands

Job description

Infoya is a global IT solutions provider specializing in transforming complex challenges into streamlined, AI-powered outcomes. Through proprietary technology accelerators and full-scale enterprise services, Infoya automates workflows, enhances operational efficiency, and drives digital transformation across industries. With a presence in Canada, the US, India, and Costa Rica, we blend technical depth with creative problem-solving to deliver measurable impact.

Job Description

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

Office Location: Toronto

Employment Type: Permanent

Work Arrangement: Hybrid (2 days in office)

Position Responsibilities:

• Design, build, and operate LLM agent systemsusing 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 cloudinfrastructure, and post-launch monitoring.
  
• Build agent tools as MCP servers and integratewith REST APIs.
  
• Address production concerns such as tooltimeouts, oversized responses, retries, and token costs.
  
• Develop or improve time-series forecasting andanomaly detection using production metrics, such as Prometheus or Grafana data.
  
• Tune detection thresholds against real incidentsand 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 throughplugins, skills, slash commands, hooks, or MCP integrations.
  
• Review and verify AI-generated code before itships.
  
• Apply safeguards to AI systems, includingcode-enforced access controls, PII and secrets handling, audit logging, costlimits, and model output evaluation.
  
• Work with security and compliance teams throughinternal review processes.
  
• Identifyopportunities by observing how engineering and operations teams work, thenprototype 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 systemto production within a company.
  
• End-to-end experience with a production agentsystem, including architecture, implementation, deployment through CI/CD toKubernetes or cloud, and monitoring after launch.
  
• Experience building agent tools as MCP serversand integrating with REST APIs.
  
• Understanding of production issues such as tooltimeouts, oversized responses, retries, and token cost management.
  
• Hands-on experience with time-series forecastingor anomaly detection on real production metrics.
  
• Experience tuning alert thresholds against realincidents and managing the trade-off between false positives and missed alerts.
  
• Regular use of AI coding tools as part of dailyengineering work, with a disciplined approach to reviewing and validatinggenerated code.
  
• Practical experience making AI systems safe forenterprise use, including access controls enforced in code, PII and secretshandling, audit logging, cost limits, and model evaluation.
  
• Comfort working within security and compliancereview processes.
  

Preferred Qualifications:

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

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

The final compensation offered will depend on local marketconditions and geographic location, as well as job-related factors such as thecandidate’s knowledge, skills, qualifications, relevant experience, andeducation/training. Compensation may also include additional components such asbenefits, and/or other incentives, where applicable. In accordance with newemployment standards requirements, we retain copies of this job posting andapplicant information for three (3) years after the posting is removed. We donot use AI technology; all applications are also reviewed by our recruitmentteam.