Deployment Engineer

Senior Deployment Engineer to lead the deployment, automation, and operational enablement of enterprise‑scale cloud, data, and AI platforms. This role is critical to ensuring production‑ready, secure, and scalable deployments across Client analytics, AI, and digital platforms.

The engineer will work closely with Client cloud platform, data engineering, AI, DevOps, and security teams. The role requires deep hands‑on expertise combined with the ability to own deployment strategy, improve platform standards.

Key Responsibilities

Enterprise Platform Deployment Leadership

• Lead end‑to‑end deployment of complex cloud, data, and AI platforms across mostly Azure but also AWS
• Own deployment architecture, standards, and operational readiness for non‑prod and production environments
• Serve as the senior escalation point for deployment‑related failures, instability, or performance issues

DevOps & CI/CD Excellence

• Design, build, and optimize enterprise‑grade CI/CD pipelines for application, data, and AI workloads
• Establish and enforce deployment best practices including versioning, rollback strategies, and environment parity
• Drive automation to minimize manual deployment effort and reduce operational risk

Kubernetes & Container Platforms

• Lead deployment and operations of containerized platforms on AKS (Azure Kubernetes Service)
• Manage cluster configuration, scaling, ingress/egress, secrets, and workload isolation
• Support container security, resilience, and high availability standards

Data & Analytics Platform Enablement

• Own deployment and operationalization of Databricks and Microsoft Fabric environments
• Support enterprise data workloads including Lakehouse architectures, analytics pipelines, and platform integrations
• Partner with data engineering teams to ensure deployments are optimized for scale, cost, and performance

AI Platform Deployment

• Lead deployment of AI solutions using Azure OpenAI Service
• Support environment configuration, endpoint management, security controls, and production hardening
• Operationalize AI workloads responsibly and securely

Infrastructure as Code & Cloud Engineering

• Build and maintain Infrastructure as Code (IaC) using Terraform, ARM/Bicep, or CloudFormation
• Ensure cloud resources follow enterprise security, networking, and governance standards
• Optimize cloud environments for cost efficiency, performance, and reliability

Security, Monitoring & Compliance

• Implement and enforce cloud security best practices (IAM, secrets, encryption, network isolation)
• Own monitoring, logging, and alerting strategy across deployed platforms
• Support audits, compliance reviews, and production readiness validations in regulated environments

Required Technical Skills

Core Expertise (Senior‑Level)

• DevOps & Deployment Engineering in large‑scale enterprise environments
• Azure Cloud (strong experience across compute, networking, security, and governance)
• AKS (Azure Kubernetes Service) – production deployment and operations
• Databricks – enterprise deployment and platform enablement
• Microsoft Fabric – environment setup and operational support
• AI Platforms – experience deploying solutions using Azure OpenAI Service
• AWS Cloud – hands‑on deployment and operational understanding

Tooling & Technologies

• CI/CD platforms (Azure DevOps, GitHub Actions, Jenkins, or equivalent)
• Infrastructure as Code: Terraform, ARM templates, Bicep, CloudFormation
• Containers & orchestration: Docker, Kubernetes
• Monitoring & observability: Azure Monitor, Log Analytics, CloudWatch, Prometheus

Qualifications & Experience

• 8+ years of experience in Cloud, DevOps, or Deployment Engineering roles
• Proven experience leading deployments for data, analytics, and AI platforms
• Strong background supporting production enterprise systems
• Experience in regulated or audit‑driven environments is highly preferred
• Ability to operate independently and lead complex deployment initiatives end‑to‑end