Hi Associates,
I hope you are doing well.
We are actively working on an urgent requirement with one of our key clients and believe your background could be a strong fit. Please share your updated resume in word format (.doc/.docx), along with your contact number and your availability for a quick discussion.
This is a time-sensitive opportunity, and we are moving quickly with submissions. I would appreciate
your prompt response so we can discuss the role in detail.
Looking forward to hearing from you soon.
Job Title: Azure AI Gateway & Platform Architect (AIOps Lead)
Location: Remote
Job Type: Long Term Project/Fulltime
Experience:
12+ Years (Cloud, Platform Engineering, AI Platform Architecture, AIOps)
Role Summary:
We are seeking an experienced Azure AI Gateway & Platform Architect (AIOps Lead) to establish and operationalize an enterprise-scale AI platform on Azure. The role will be responsible for designing and implementing AI governance frameworks, Azure AI Gateway architecture, secure AI service consumption, observability, reliability engineering, and AI-driven operations (AIOps).
The ideal candidate will have deep expertise in Azure Cloud, Azure OpenAI, API Management, AI platform engineering, monitoring, automation, and large-scale enterprise platform deployment. The individual will drive the creation of a secure, scalable, and governed AI consumption model while enabling business teams to build and deploy GenAI solutions efficiently.
Key Responsibilities:
Azure AI Platform Establishment
- Design and establish an enterprise-grade Azure AI Platform.
- Define architecture standards, landing zones, governance controls, and reference architectures for AI workloads.
- Create reusable platform patterns for GenAI, RAG, Agentic AI, and AI-assisted automation solutions.
- Enable secure onboarding of business units and development teams onto the AI platform.
- Define enterprise AI operating model, platform lifecycle, and service management framework.
Azure AI Gateway Architecture
- Design and implement Azure AI Gateway strategy leveraging Azure API Management.
- Establish centralized routing, throttling, cost management, security, monitoring, and policy enforcement for AI services.
- Build abstraction layers for Azure OpenAI, third-party LLMs, embedding models, vector databases, and AI services.
- Implement AI service catalog and model management framework.
- Enable multi-model orchestration and model governance.
AI Governance & Security
- Define enterprise AI governance standards.
- Implement responsible AI controls, security guardrails, auditability, and compliance requirements.
- Establish identity management, RBAC, secrets management, and access controls.
- Drive implementation of data protection, AI risk management, and regulatory compliance practices.
- Partner with security teams to review AI workloads and platform architecture.
AIOps & Intelligent Operations
- Establish enterprise AIOps framework using Azure Monitor, Log Analytics, Application Insights, and Open Telemetry.
- Implement AI-driven anomaly detection, predictive analytics, root cause analysis, and automated remediation.
- Design self-healing operational workflows and intelligent incident management processes.
- Build operational dashboards, observability platforms, and reliability metrics.
- Reduce MTTR, improve service availability, and automate operational response activities.
Platform Engineering & Automation
- Develop Infrastructure-as-Code solutions using Terraform/Bicep.
- Automate deployment, configuration, governance, and compliance validation.
- Enable CI/CD integration for AI services and platform components.
- Implement platform monitoring, health checks, capacity planning, and performance optimization.
Stakeholder Management
- Work closely with Enterprise Architecture, Cloud Engineering, Security, Data, and AI Engineering teams.
- Establish architecture review processes and platform governance councils.
- Present roadmap, architecture, and operational metrics to leadership and customer stakeholders.
- Mentor engineering teams on AI platform best practices.
Required Skills:
Azure Cloud
- Azure Landing Zones
- Azure Resource Manager
- Azure Networking
- Azure Kubernetes Services (AKS)
- Azure Functions
- Azure App Services
- Azure Storage Services
- Azure Identity & Access Management
AI & GenAI
- Azure OpenAI Service
- Agentic AI Architecture
- AI Gateway Patterns
- Prompt Engineering
- RAG Architecture
- Vector Databases
- AI Model Governance
- LLM Deployment & Operations
API & Integration
- Azure API Management (APIM)
- REST APIs
- Event-Driven Architecture
- Microservices
- Service Mesh
- Enterprise Integration Patterns
AIOps & Observability
- Azure Monitor
- Application Insights
- Log Analytics
- Open Telemetry
- Prometheus / Grafana
- Intelligent Alerting
- Incident Automation
- Root Cause Analysis
- Self-Healing Automation
DevSecOps
- Azure DevOps / GitHub
- CI/CD Pipelines
- Terraform / Bicep
- Policy as Code
- Security Automation
- Infrastructure as Code
Preferred Qualifications:
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure AI Engineer Associate
- Azure DevOps Expert Certification
- Experience implementing Azure OpenAI platforms for Banking or Financial Services clients.
- Experience establishing enterprise AI Centers of Excellence (CoE).
- Experience with GitLab Duo, GitHub Copilot, MCP, Agentic AI, and Enterprise AI Governance.
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