Position Summary

We are seeking an experienced Principal Architect to lead the design and implementation of an enterprise-grade Centralized Observability, AIOps, and Self-Healing Platform. The platform will unify metrics, logs, traces, events, and infrastructure telemetry across cloud and on-premises environments while leveraging AI/ML for anomaly detection, intelligent alerting, root cause analysis, and automated remediation.

The ideal candidate has deep expertise in Observability, Platform Engineering, Cloud Architecture, SRE, Kubernetes, Event-Driven Architecture, AI/ML integration, and Automation.

Key Responsibilities

Platform Architecture

• Design enterprise-wide centralized observability architecture.
• Define platform standards and reference architectures.
• Build a multi-tenant observability platform supporting multiple customers and business units.
• Design for high availability, scalability, and resilience.
• Establish governance, onboarding standards, and platform lifecycle management.

Observability Architecture

Design and standardize:

• Metrics collection
• Distributed tracing
• Centralized logging
• Event correlation
• Synthetic monitoring
• Real User Monitoring (RUM)
• Infrastructure monitoring
• Application Performance Monitoring (APM)
• Database observability
• Network observability

Implement observability using tools such as:

• Prometheus
• Grafana
• OpenTelemetry
• Datadog
• Splunk
• CloudWatch
• Loki
• Tempo
• Jaeger
• Elasticsearch

AIOps

Design AI-driven capabilities including:

• Intelligent alert correlation
• Event deduplication
• Dynamic thresholding
• Anomaly detection
• Predictive analytics
• Capacity forecasting
• Root cause analysis
• Incident prioritization
• Service dependency mapping
• Change impact analysis

Self-Healing Platform

Design automated remediation workflows for:

• Kubernetes pod failures
• Container restarts
• Node failures
• Database connectivity issues
• Memory leaks
• Disk space issues
• High CPU utilization
• Service failures
• Network issues
• Certificate expiry
• Auto-scaling
• Rollback automation

Integrate with:

• Rundeck
• StackStorm
• Ansible
• Terraform
• Kubernetes Operators
• Argo Workflows
• GitHub Actions
• Jenkins

Cloud & Platform Engineering

Architect solutions across:

• AWS
• Azure
• Google Cloud Platform
• Kubernetes
• OpenShift
• Docker
• Service Mesh (Istio, Linkerd)

Design:

• Multi-cluster observability
• Multi-region deployment
• Hybrid cloud observability
• Disaster recovery

AI Integration

Build AI capabilities using:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Vector databases
• AI agents
• Knowledge graphs
• Model orchestration
• AI-assisted runbooks
• Automated incident summarization
• Conversational operations assistants

Experience with:

• OpenAI-compatible APIs
• Amazon Bedrock
• Azure OpenAI
• Google Vertex AI
• LangGraph, LangChain, or similar orchestration frameworks

Event-Driven Architecture

Design integrations using:

• Apache Kafka
• IBM MQ
• RabbitMQ
• Amazon EventBridge
• Event-driven microservices

SRE Practices

Implement:

• SLIs
• SLOs
• Error budgets
• Incident management
• Chaos engineering
• Reliability engineering
• Capacity planning
• Production readiness reviews

Security

Implement:

• RBAC
• OAuth2 / OIDC
• mTLS
• Secrets management
• Audit logging
• Zero Trust principles
• Compliance controls

Required Technical Skills

Observability

• Prometheus
• Grafana
• OpenTelemetry
• Datadog
• Splunk
• Loki
• Tempo
• Jaeger
• Elasticsearch

Cloud

• AWS
• Azure
• Google Cloud Platform

Kubernetes

• Kubernetes
• OpenShift
• Helm
• Argo CD

Automation

• Terraform
• Ansible
• Python
• Bash
• PowerShell

Programming

• Python
• Go
• Java
• Node.js

AI/ML

• LLM integration
• RAG
• Vector databases
• ML-based anomaly detection
• AI agents

Messaging

• Kafka
• IBM MQ
• RabbitMQ

Databases

• PostgreSQL
• MySQL
• MongoDB
• Redis

Preferred Experience

• Banking / Financial Services
• Insurance
• Healthcare
• Enterprise SaaS
• Multi-cloud environments
• Large-scale production operations

Responsibilities

The successful candidate will:

• Define the enterprise observability strategy.
• Build a centralized telemetry platform.
• Design AI-driven incident detection and correlation.
• Implement automated self-healing workflows.
• Standardize dashboards, alerts, and telemetry collection.
• Establish SRE best practices.
• Create reusable platform components.
• Mentor engineering teams.
• Lead architecture reviews.
• Drive cloud modernization initiatives.

Nice-to-Have Skills

• OpenTelemetry Collector customization
• eBPF-based observability
• FinOps
• ServiceNow integration
• PagerDuty integration
• Opsgenie integration
• Knowledge graph technologies
• Digital twins for IT operations
• MLOps experience

Leadership Skills

• Enterprise architecture
• Executive communication
• Technical mentoring
• Cross-functional leadership
• Vendor evaluation
• Strategic roadmap planning

Success Metrics

The architect will be expected to deliver:

• A single-pane-of-glass observability platform.
• Reduction in Mean Time to Detect (MTTD).
• Reduction in Mean Time to Resolve (MTTR).
• Intelligent alert noise reduction.
• Automated remediation for common operational issues.
• Enterprise-wide telemetry standards.
• High platform availability and scalability.
• Improved developer and operations productivity.