Full - Stack Software Developer at gaiia enhancing cloud infrastructure and optimizing public APIs for better developer experience. Collaborating with teams to deliver innovative technical solutions.
About the role
- Senior AI Platform Operations Engineer responsible for AI platform reliability and operations at the organization. Ensuring compliance and continuous improvement for AI services and solutions.
Responsibilities
- Administer and operate the AI platform to ensure availability, performance, and resilience across environments, integrations, and supporting infrastructure.
- Monitor platform health using dashboards, logs, metrics, and alerts, and coordinate incident and service restoration activities.
- Lead operational triage, escalation coordination, and post-incident reviews to strengthen services stability and resilience.
- Track and report on service reliability indicators, incident trends, and operational performance.
- Enable approved AI use cases into production by ensuring environment readiness, dependency validation, completion of operational readiness checklists, and structured service transition activities.
- Support platform lifecycle management through release coordination, change readiness validation, and maintenance and capacity planning.
- Ensure AI platform changes meet defined operational and control readiness criteria prior to release.
- Implement and maintain observability capabilities, including telemetry, logging, metrics, and traces required for enterprise AI operations.
- Analyze operational data to identify anomalies, recurring issues, root-cause patterns.
- Implement AI Ops use cases such as alert correlation, anomaly detection, root-cause support, forecasting and predictive insights, automation of repetitive operational tasks.
- Continuously improve operational efficiency through targeted automation and process optimization.
- Execute governance controls for AI solutions, including usage and access controls, data privacy considerations, auditability and traceability, human oversight requirements.
- Ensure operational practices align with enterprise security policies, risk controls, and compliance requirements.
- Maintain documentation and evidence required for audit, governance reviews, production readiness checkpoints, and control validation.
- Identify control gaps and escalate risks appropriately to relevant governance and risk stakeholders.
- Maintain operational visibility of AI platform assets required for monitoring, support, and cost alignment.
- Validate asset ownership, relationships, and lifecycle status in collaboration with application and platform owners.
- Support ongoing audits to ensure AI assets and associated cost attribution remain accurate and current.
Requirements
- University degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
- 5-7 years of experience in platform operations, site reliability engineering, DevOps, cloud operations, or enterprise IT operations.
- Strong experience supporting production platforms and services, including monitoring, incident response, problem management, service restoration, and operational reporting.
- Experience with cloud platforms, observability, automation, configuration management, and integration patterns, including Azure Automation runbooks (PowerShell/Python), Azure AI, Copilot integrations, AKS, virtual networks (hub-and-spoke), and App Service.
- Expertise with observability tools such as Azure Monitor, Application Insights, and Grafana.
- Experience with CI/CD and automation tools such as Azure DevOps, GitHub Actions, and Logic Apps.
- Knowledge of configuration management and infrastructure-as-code tools such as Bicep, Terraform, Azure Policy, Key Vault, and relevant open-source technologies.
- Knowledge of integration and event-driven technologies such as API Management, open-source API tools, Service Bus, Event Grid, and Apache Kafka.
- Working knowledge of platform-supporting data and search services such as Elastic, Azure AI Search, and Cosmos DB.
- Knowledge of enterprise network, edge security, and related internal platforms such as DNA, Fortinet, and Akamai is an asset.
- Working knowledge of AI/ML operational concepts, including model lifecycle support, telemetry, governance controls, human-in-the-loop practices, and production monitoring.
- Strong understanding of ITIL/ITSM processes, including change, release, incident, problem, configuration, and service reporting practices.
- Analytical and structured thinker with strong troubleshooting, root-cause analysis, prioritization, and continuous improvement skills.
- Strong service orientation, professional maturity, and the ability to collaborate effectively across operations, engineering, security, risk, data, and business teams.
- Experience creating technical documentation, operational procedures, support playbooks, dashboards, and user guidance materials.
- Knowledge of security, privacy, audit, and compliance considerations relevant to enterprise AI and platform operations.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
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