What are the responsibilities and job description for the Senior Deployment Engineer position at Mind Ware Inc?
Hiring for Senior Deployment Engineer Cloud, Data & AI Platforms @ Montvale, NJ for a W2 contract position
Role Summary
Client 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's analytics, AI, and digital platforms. The engineer will work closely with client's 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 nonprod 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 lake house 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 largescale 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 handson 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 auditdriven environments is highly preferred
- Ability to operate independently and lead complex deployment initiatives endtoend
Mandatory: Azure, AWS, AI, DevOps, CI/CD, Azure Kubernetes Service (AKS), Databricks, Fabric, OpenAI |
Good to Have: Kafka, Agentic AI, Analytics Tools Power BI, AI & ML Innovation