Deployment Engineer

Orion Innovation•Montvale, NJ

About The Position

Orion Innovation is seeking a 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 and improve platform standards.

Requirements

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
Ability to operate independently and lead complex deployment initiatives end-to-end
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
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

Nice To Haves

Experience in regulated or audit-driven environments is highly preferred

Responsibilities

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
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
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
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
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
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
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