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Apptoza Inc.

Senior Observability Engineer

Toronto, ON, Canada

Job type: Contract
Apply by: Oct 25, 2026
Posting language: English
Posted: Sep 25, 2026

Apply About employer

Applications open in a new tab.

Original job description

Senior Observability Engineer (KUBERNETES & CONTAINERS, GITOPS & AUTOMATION, QUERY LANGUAGES, CLOUD & INFRASTRUCTURE)

Toronto, ON - Hybrid (4 Days WFO)

12 months

ABOUT THE ROLE

We are seeking an experienced Observability Engineer to join our Enterprise

Kubernetes Platform team at a leading financial services organization. You'll

own the complete observability stack across 50+ production Kubernetes clusters,

providing metrics, logging, tracing, and alerting capabilities that ensure

exceptional reliability and performance for mission-critical applications.

This role combines deep technical expertise in modern observability tools with

emerging AI/ML capabilities to build intelligent monitoring solutions,

predictive alerting, and self-healing infrastructure.

========================================================================

WHAT YOU'LL DO

========================================================================

OBSERVABILITY STACK OWNERSHIP

--------------------------------------------------------------------------------

Design, deploy, and maintain enterprise-scale observability infrastructure

including Prometheus, Grafana, Thanos, Loki, and modern collection agents

Manage observability deployments using GitOps principles and infrastructure

as code

Implement long-term metrics storage solutions with cloud object storage

Maintain and upgrade observability components across development, QA, UAT,

production, and DR environments

Configure distributed observability architecture spanning multiple data

centers and cloud providers

METRICS & MONITORING

--------------------------------------------------------------------------------

Design and implement Prometheus monitoring strategies for Kubernetes

infrastructure and containerized applications

Create ServiceMonitors, PodMonitors for automated metrics collection

Develop rules for intelligent alerting with minimal false positives

Configure multi-cluster metrics federation and aggregation

Optimize metrics cardinality, storage e_iciency, and query performance

Implement recording rules for pre-aggregated metrics and SLI calculations

DASHBOARDS & VISUALIZATION

--------------------------------------------------------------------------------

Build comprehensive Grafana dashboards for infrastructure health, application

performance, and business metrics

Create reusable dashboard templates and libraries for development teams

Implement dashboard-as-code

Configure multiple datasources

Design executive dashboards with SLO/SLI tracking and business KPIs

Implement role-based access control and multi-tenancy in Grafana

LOGGING INFRASTRUCTURE

--------------------------------------------------------------------------------

Deploy and manage centralized logging solutions (Loki, ELK, Splunk, or

similar)

Configure log collection agents (Promtail, Fluentd, FluentBit, Vector, etc.)

Design log retention policies balancing cost, compliance, and operational

needs

Create LogQL/Lucene queries and log-based alerts

Implement log correlation with metrics and traces for unified troubleshooting

Build log aggregation pipelines with parsing, filtering, and enrichment

ALERTING & INCIDENT MANAGEMENT

--------------------------------------------------------------------------------

Configure intelligent alerting with Alertmanager or equivalent platforms

Design alert rules with appropriate severity levels, thresholds, and SLOs

Implement alert routing to Slack, PagerDuty, ServiceNow, email, and webhooks

Create automated runbooks and remediation workflows

Develop alert inhibition, silencing, and grouping strategies

Tune alerting to achieve signal-to-noise ratio improvements

Integrate with incident management and on-call rotation systems

DISTRIBUTED TRACING

--------------------------------------------------------------------------------

Implement distributed tracing solutions using OpenTelemetry, Jaeger, or

Tempo

Instrument applications for trace collection and correlation

Configure trace sampling strategies for cost and performance optimization

Build trace-based dashboards for latency analysis and dependency mapping

Integrate tracing with metrics and logs for comprehensive observability

AI/ML FOR OBSERVABILITY

--------------------------------------------------------------------------------

Implement AI-powered anomaly detection for metrics and logs

Build predictive alerting using machine learning models to forecast issues

before they occur

Develop intelligent alert correlation and root cause analysis systems

Integrate LLM-based tools for log analysis and troubleshooting assistance

Implement AIOps capabilities for automated incident triage and resolution

Use AI to optimize alert thresholds and reduce false positives

Build natural language query interfaces for observability data

Implement Model Context Protocol (MCP) for AI agent integration with

observability platforms

AUTOMATION & PLATFORM INTEGRATION

--------------------------------------------------------------------------------

Automate observability deployment using GitOps workflows (FluxCD, ArgoCD)

Integrate with CI/CD pipelines for automated testing and validation

Build self-service portals for teams to create dashboards and alerts

Develop APIs and CLIs for observability automation

Integrate with secrets management solutions (Vault, AWS Secrets Manager)

Configure LDAP/AD/SSO authentication for observability platforms

Automate compliance reporting and audit logging

ENABLEMENT & COLLABORATION

--------------------------------------------------------------------------------

Onboard application teams to observability platforms

Provide guidance on instrumentation best practices

Create documentation, training materials, and self-service guides

Conduct workshops

Support development teams during incidents with observability insights

Collaborate with SRE, DevOps, and platform engineering teams

PERFORMANCE & COST OPTIMIZATION

--------------------------------------------------------------------------------

Monitor and optimize observability stack resource consumption

Implement autoscaling for stateless observability components

Tune data retention, compaction, and downsampling strategies

Conduct capacity planning for metrics and log storage growth

Optimize query performance and dashboard response times

Implement cost allocation and chargeback for multi-tenant environments

=======================================================================

REQUIRED QUALIFICATIONS

=======================================================================

EXPERIENCE

--------------------------------------------------------------------------------

4-6 years of experience in observability, monitoring, SRE, or platform

engineering roles

3+ years hands-on production experience with Prometheus and Grafana

2+ years working with Kubernetes and containerized environments

Strong expertise with PromQL for metrics querying and alerting

Experience deploying and managing observability stacks at scale (1000+ nodes)

Proven track record of reducing MTTR through e_ective observability

CORE TECHNICAL SKILLS

--------------------------------------------------------------------------------

OBSERVABILITY PLATFORMS:

Prometheus (including Prometheus Operator)

Grafana (dashboards, alerting, plugins)

Thanos, Cortex, or Mimir for long-term storage

Alertmanager or equivalent alerting platforms

LOGGING:

Loki, Elasticsearch/ELK Stack, Splunk, or CloudWatch Logs

Log collection agents (Promtail, Fluentd, FluentBit, Vector)

LogQL, Lucene, or equivalent query languages

TRACING:

OpenTelemetry (OTEL Collector, instrumentation)

Jaeger, Zipkin, Tempo, or AWS X-Ray

Trace sampling and correlation strategies

KUBERNETES & CONTAINERS:

Kubernetes architecture and operations (1.24+)

Custom Resource Definitions (CRDs) and Operators

ServiceMonitor, PodMonitor, PrometheusRule resources

Kubernetes metrics (kube-state-metrics, node-exporter, cAdvisor)

GITOPS & AUTOMATION:

GitOps workflows (FluxCD, ArgoCD, or similar)

Infrastructure as Code (Kustomize, Helm, Terraform)

CI/CD platforms (GitHub Actions, GitLab CI, Jenkins)

Scripting (Python, Bash, Go)

QUERY LANGUAGES:

PromQL (expert level required)

LogQL or Lucene

Basic SQL for data analysis

CLOUD & INFRASTRUCTURE:

AWS, Azure, or GCP cloud platforms

Object storage (S3, GCS, Azure Blob)

Multi-cloud and hybrid architectures

vSphere or on-premises virtualization (nice to have)

AI/ML & EMERGING TECHNOLOGIES

--------------------------------------------------------------------------------

Experience with AI/ML frameworks for observability (Prophet, TensorFlow,

PyTorch)

Anomaly detection algorithms and time-series forecasting

LLM integration for log analysis and troubleshooting (GPT, Claude, etc.)

Model Context Protocol (MCP) for AI agent integration

AIOps platforms (Moogsoft, BigPanda, Datadog Watchdog, etc.)

Natural language processing for log parsing and analysis

Familiarity with vector databases for semantic search (Pinecone, Weaviate)

Experience with AI-powered root cause analysis tools

Knowledge of prompt engineering for observability use cases

PREFERRED QUALIFICATIONS

--------------------------------------------------------------------------------

Kubernetes certifications (CKA, CKAD, or CKS)

Grafana certification or equivalent training

Knowledge of service mesh observability (

Experience with SRE practices, SLIs, SLOs, and error budgets

Background in financial services or regulated industries

Familiarity with compliance requirements (SOX, PCI-DSS, etc.)

Contributions to open-source observability projects

Experience with APM tools

Thanks & regards,

Lakshmi Bhavani

Apptoza Inc.

Phone: 437-291-3224 Ext 1105

Mail id : Lakshmi.bhavani@apptoza.com

Apply About employer

More about this job

Responsibilities

Own and operate enterprise observability across production Kubernetes clusters, including metrics, dashboards, logging, tracing, alerting, and long-term data storage. Automate and optimize the platform, develop AI/ML-enabled monitoring and incident workflows, and enable application teams through guidance, documentation, and collaboration.

Requirements

Requires 4–6 years of observability, monitoring, SRE, or platform engineering experience, including 3+ years with Prometheus and Grafana and 2+ years with Kubernetes. Candidates should have expert PromQL skills and experience operating large-scale observability stacks, with knowledge of logging, tracing, GitOps, infrastructure as code, cloud platforms, and AI/ML observability capabilities.

Skills

Prometheus
Grafana
Kubernetes
PromQL
Thanos
Loki
OpenTelemetry
Distributed Tracing
GitOps
Infrastructure As Code
Alertmanager
LogQL
Python
Cloud Platforms
AI/ML Anomaly Detection
Incident Management

Visa sponsorship

Not detected in the job text

Hiring contact

Lakshmi Bhavani

Hiring contact email

Lakshmi.bhavani@apptoza.com

Keywords

Prometheus
Grafana
Thanos
Cortex
Mimir
Loki
Elasticsearch
Splunk
OpenTelemetry
Jaeger
Tempo
Kubernetes
PromQL
LogQL
Alertmanager
GitOps
FluxCD
ArgoCD
Terraform
Helm
Python
AWS
Azure
GCP
Object Storage
AI/ML
Anomaly Detection
AIOps
Model Context Protocol
SRE

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