The AI Platform & DevOps Engineer is responsible for building, managing, and optimizing the cloud infrastructure, DevOps processes, deployment pipelines, security controls, observability, and operational foundations required to support enterprise AI solutions. This role ensures AI applications can transition efficiently from proof of concept to production through secure, scalable, and highly available platforms.

The successful candidate will establish best practices for infrastructure automation, CI/CD, monitoring, cost optimization, identity management, and production support while enabling AI engineering teams to deliver reliable, enterprise-ready solutions.

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Key Responsibilities

1. AI Platform Engineering

• Design, build, and maintain development, testing, staging, and production environments for AI applications.
• Deploy and manage AI platforms, application hosting environments, containers, and cloud infrastructure.
• Develop reusable deployment templates, infrastructure standards, and configuration management practices.
• Manage platform services including databases, vector stores, search services, API gateways, storage, and application hosting.
• Ensure secure connectivity between AI applications and enterprise systems.

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2. DevOps & Release Engineering

• Design and maintain CI/CD pipelines for AI applications, APIs, backend services, and web applications.
• Automate build, testing, deployment, rollback, and release management processes.
• Implement source control strategies, versioning, environment promotion, and release governance.
• Integrate automated testing, security scanning, and quality assurance into deployment pipelines.
• Support change management processes and ensure controlled software releases.

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3. Observability, Reliability & Operations

• Implement centralized logging, monitoring, dashboards, and alerting for AI applications and cloud platforms.
• Monitor system performance including:
  • Availability
  • Latency
  • Error rates
  • Throughput
  • Resource utilization
  • API consumption
  • Operational costs
• Develop operational runbooks, incident response procedures, and production support documentation.
• Troubleshoot deployment, networking, infrastructure, and application performance issues.
• Support Service Level Agreements (SLAs) and Service Level Objectives (SLOs) for AI services.

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4. Security, Compliance & Cost Management

• Implement identity and access management, Role-Based Access Control (RBAC), secrets management, and secure networking practices.
• Collaborate with cybersecurity teams to support vulnerability management, penetration testing, and security assessments.
• Ensure secure deployment of AI applications and enterprise data integrations.
• Monitor cloud resource utilization and optimize infrastructure and AI service costs.
• Maintain audit trails and support governance, compliance, and responsible AI controls.

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5. Platform Enablement & Continuous Improvement

• Develop reusable deployment templates, automation scripts, and platform standards.
• Document platform architecture, deployment procedures, and operational guidelines.
• Train engineering teams on DevOps practices, CI/CD, monitoring, security, and cloud operations.
• Support onboarding of internal teams and technology partners.
• Continuously improve platform performance, automation, and operational efficiency.

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Technical Skills

Cloud Platforms

• Microsoft Azure or equivalent cloud platforms
• Cloud application hosting
• Serverless services
• Cloud networking
• Storage services

DevOps

• Azure DevOps
• GitHub Actions
• Git
• CI/CD Pipelines
• Infrastructure Automation

Containers & Orchestration

• Docker
• Kubernetes
• Container Platforms

Infrastructure as Code

• Terraform
• Bicep
• ARM Templates
• Other Infrastructure-as-Code tools

Monitoring & Observability

• Azure Monitor
• Application Insights
• Log Analytics
• Grafana
• Prometheus
• Similar monitoring platforms

Security

• Identity & Access Management (IAM)
• OAuth
• RBAC
• Secrets Management
• Key Vault
• Secure Networking
• API Security

Programming & Automation

• Python
• Bash
• PowerShell
• Linux Administration
• Automation Scripting

AI Platform Technologies

• AI application hosting
• Model endpoints
• API gateways
• Vector databases
• Enterprise search services
• AI infrastructure management

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Qualifications & Experience

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, or a related discipline.
• Minimum 5 years of experience in DevOps, Cloud Engineering, Platform Engineering, Site Reliability Engineering (SRE), or Infrastructure Engineering.
• Minimum 2 years supporting cloud-native enterprise applications or platforms.
• Proven experience implementing CI/CD pipelines and infrastructure automation.
• Experience with monitoring, logging, alerting, and production support.
• Strong understanding of cloud security, identity management, secrets management, and access controls.
• Exposure to AI platforms, APIs, cloud-native applications, or modern enterprise application environments.

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Preferred Qualifications

The following qualifications or experience are considered advantageous:

• Cloud Platform Certifications
• DevOps Certifications
• Kubernetes Certifications
• Azure Administrator or Azure DevOps certifications
• Experience with:
  • AI/ML platforms
  • Large Language Models (LLMs)
  • AI application deployment
  • Enterprise search
  • Vector databases
  • API Management
  • Financial Services
  • Banking
  • Telecommunications
  • Enterprise Technology
  • Cost optimization and cloud governance

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Core Competencies

• Cloud Infrastructure Management
• DevOps Engineering
• Platform Engineering
• Automation
• Problem Solving
• Security Awareness
• Continuous Improvement
• Analytical Thinking
• Team Collaboration
• Communication Skills
• Stakeholder Management
• Planning & Organization
• Adaptability
• Attention to Detail

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Key Performance Indicators (KPIs)

Performance will be measured against:

• Deployment automation and reliability
• Platform availability and operational stability
• CI/CD pipeline efficiency
• Security and compliance adherence
• Monitoring and observability effectiveness
• Infrastructure performance and scalability
• Cloud and AI cost optimization
• Incident response and resolution
• Developer enablement and platform usability

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Career Development

Potential career progression includes:

• Senior Platform Engineer
• AI Platform Lead
• DevOps Lead
• Site Reliability Engineering (SRE) Manager
• AI Infrastructure Architect
• Cloud Solutions Architect
• Principal Platform Engineer
• Head of Platform Engineering