Ai platform & devops engineer

The AI Platform & Dev Ops Engineer is responsible for building, managing, and optimizing the cloud infrastructure, Dev Ops 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.

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. 2. Dev Ops & 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. 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. 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. 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 Dev Ops practices, CI/CD, monitoring, security, and cloud operations. Support onboarding of internal teams and technology partners. Continuously improve platform performance, automation, and operational efficiency. Technical Skills Cloud Platforms Microsoft Azure or equivalent cloud platforms Cloud application hosting Serverless services Cloud networking Storage services Dev Ops Azure Dev Ops Git Hub 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 Power Shell Linux Administration Automation Scripting AI Platform Technologies AI application hosting Model endpoints API gateways Vector databases Enterprise search services AI infrastructure management Qualifications & Experience Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Cloud Computing, or a related discipline. Minimum 5 years of experience in Dev Ops, 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. Preferred Qualifications

The following qualifications or experience are considered advantageous:

Cloud Platform Certifications Dev Ops Certifications Kubernetes Certifications Azure Administrator or Azure Dev Ops 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 Core Competencies Cloud Infrastructure Management Dev Ops Engineering Platform Engineering Automation Problem Solving Security Awareness Continuous Improvement Analytical Thinking Team Collaboration Communication Skills Stakeholder Management Planning & Organization Adaptability Attention to Detail 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 Career Development

Potential career progression includes:

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