Data Center Architect

**What success looks like in this role**:
DC Architect is responsible for designing, implementing, and optimizing modern data center infrastructures. This role requires expertise in data center networking, cloud integration, high-performance computing, and AI-driven automation. The architect will ensure high availability, scalability, security, and efficiency of data center operations, leveraging the latest AI technologies for automation, predictive maintenance, and energy efficiency.
- Provides Solutions Architecture consultation and advice Unisys-wide.
- Works with clients, Product Owners, Sales & Sales Excellence teams and other stakeholders to align the architectural direction of solution intent.
- Serves as lead on large-scale solution and component development, engaging with cross-functional leaders and stakeholders to ensure mutual understanding, ongoing communication and alignment of outcome expectations.
- Conducts or leads studies to determine the economic, technical and organizational feasibility of proposed solutions.

**Key Responsibilities**:
1. Data Center Architecture & Design:

- Design and develop scalable, resilient, and high-performance data center architectures.
- Define and implement data center topology, including compute, storage, networking, and virtualization.
- Ensure seamless integration of on-premises and cloud-based (hybrid/multi-cloud) infrastructures.
- Develop AI-driven optimization models for workload balancing, power efficiency, and predictive scaling.

2. Implementation & Deployment:

- Oversee the deployment of data center infrastructure, including servers, storage, SDN, and hyperconverged infrastructure (HCI).
- Implement AI-powered automation tools for resource provisioning, capacity planning, and self-healing systems.
- Work closely with cross-functional teams to deploy secure, high-performance solutions.

3. Network & Security Architecture:

- Design and implement AI-optimized networking for high-speed, low-latency data transfers.
- Configure SDN, NFV, and intent-based networking (IBN) solutions for data center automation.
- Implement AI-driven security solutions, including anomaly detection, threat prediction, and automated response systems.

4. AI & Automation in Data Centers:

- Utilize AI for predictive analytics in infrastructure health monitoring and fault prediction.
- Deploy AI-powered DCIM (Data Center Infrastructure Management) solutions for automated energy optimization.
- Implement AIOps (Artificial Intelligence for IT Operations) to improve performance monitoring and troubleshooting.
- Optimize edge computing and AI workloads within data centers for faster processing and real-time analytics.

5. Cloud & Hybrid Infrastructure:

- Design cloud-native architectures, ensuring seamless hybrid cloud and multi-cloud integration.
- Optimize workloads between on-prem, edge, and cloud environments using AI-based orchestration.
- Implement containerized infrastructure with Kubernetes, OpenShift, and cloud-based AI solutions.

6. Performance Optimization & Sustainability:

- Improve energy efficiency using AI-powered cooling and power management systems.
- Ensure high availability and disaster recovery using AI-assisted fault tolerance and failover mechanisms.
- Work on Green Data Center initiatives to reduce carbon footprint using AI-driven insights.

LI-SP2

**You will be successful in this role if you have**:

- BA/BS degree and 12+ years’ relevant experience OR equivalent combination of education and experience
- Master’s degree preferred and proven skills in
- Data Center Design: Expertise in hyperconverged infrastructure (HCI), SDN, SD-WAN, and high-performance computing (HPC).
- Networking & Security: Deep knowledge of BGP, EVPN, VXLAN, firewall security, Zero Trust, and microsegmentation.
- AI & Automation: Hands-on experience with AI-driven network and data center automation tools (e.g., NVIDIA AI Enterprise, Ansible, Terraform, AI-driven DCIM).
- Cloud & Virtualization: Experience with VMware, OpenStack, Kubernetes, AWS/GCP/Azure networking.
- AI/ML in Data Centers: Knowledge of AI frameworks like TensorFlow, PyTorch, and AI hardware (NVIDIA GPUs, TPUs).
- Monitoring & Optimization: Experience with AIOps, predictive analytics, and intelligent workload management.

Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.