AI Platform Engineer

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

Job summary

Infoya Inc. in Toronto is seeking a hands-on AI Platform Engineer to design, build, and operate production-ready LLM agent systems and anomaly detection tools. You will own solutions from initial design through deployment and monitoring, applying practical AI governance.

This role requires deep systems engineering, with MCP servers, REST APIs, and containerized deployments on Kubernetes or cloud infrastructure, plus collaboration with security and compliance teams.

Qualifications

• Bachelor's degree in CS, Engineering, or related field.
• 1
• 4+ years shipping LLM agent systems to production.
• End-to-end experience from architecture to monitoring.
• Experience with MCP servers and REST APIs.
• Understanding production issues like timeouts and token costs.
• Hands-on time-series forecasting or anomaly detection on production metrics.
• Regular use of AI coding tools and code review discipline.
• Enterprise-ready AI safety, including access controls and audit logging.

Responsibilities

• Design, build, and operate LLM agent systems.
• Own delivery end to end from design to monitoring.
• Build MCP servers and REST API integrations.
• Address production concerns like timeouts and token costs.
• Develop time-series forecasting and anomaly detection.
• Tune detection thresholds for incidents vs false positives.
• Use AI coding tools daily.
• Extend AI coding tools via plugins, skills, commands, hooks, or MCP integrations.
• Review and verify AI-generated code before release.
• Apply safeguards: access controls, PII handling, audit logging, cost limits.
• Collaborate with security and compliance teams.
• Identify opportunities by observing workflows and delivering adoption-ready tools.

Skills

LLM agent systems

CI/CD

Kubernetes

AI tooling

Security compliance

Education

Bachelor's degree in CS/Engineering

Tools

Kubernetes

REST APIs

CI/CD pipelines

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

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.

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.