AI Solution Architect

Uvation

. Own end-to-end architecture for AI Factory and enterprise AI solutions from requirements through production readiness.

Posted 9/18/2026full-timeRemote • IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI/ML architecture, including GPU compute and networking design, while ensuring high-performance and secure enterprise solutions. Proficient in developing reference architectures and leading cross-functional collaboration for deployment and operational readiness.

Highest-signal resume keywords

AI/ML Architecture ExpertiseGPU Compute Architecture DesignKubernetes and Container RuntimesNVIDIA CertificationsCloud Solutions Architecture

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Applicant Tracking System Keywords

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Hard Skills

AI Factory ArchitectureGPU Scheduling and Multi-TenancyHigh-Performance NetworkingParallel File SystemsData Lake ConceptsModel Serving and Inference PlatformsPerformance EngineeringDisaster Recovery PlanningCapacity PlanningInfrastructure Telemetry

Soft Skills

Technical LeadershipCollaborationProblem-SolvingCommunication

Tools & Technologies

NVIDIA HGX/DGXCephWEKAPrometheusGrafanaOpenTelemetryAWS AI InfrastructureAzure AI InfrastructureInfiniBandNVIDIA ConnectX

Certifications & Qualifications

AWS Solutions ArchitectAzure Solutions ArchitectTOGAFCCNP/CCIECISSPKubernetes CertificationsRed Hat/Linux Certifications

Industry Keywords

AI SolutionsEnterprise ArchitectureHigh-Performance ComputingCloud SecurityZero Trust SecurityData ProtectionOperational ResilienceDisaster RecoveryCapacity ModelsPerformance Objectives

Tech Stack

Tools & technologies

AWSAzureCloudGrafanaKubernetesLinuxNFSPrometheusPyTorchTensorflow

About the role

Key responsibilities & impact

Own end-to-end architecture for AI Factory and enterprise AI solutions from requirements through production readiness.
Assess AI/ML workload requirements for training, fine-tuning, inference, batch processing and high-performance computing.
Design GPU compute architectures including NVIDIA HGX/DGX/OEM platforms, multi-GPU systems, NVLink/NVSwitch and GPU resource allocation.
Design high-performance AI networking using 100/200/400/800G Ethernet, EVPN/VXLAN and leaf-spine architectures.
Design AI storage and data architectures using object storage and parallel file systems such as Ceph and WEKA.
Define AI platform architecture across Kubernetes, HPC, container runtimes, model-serving platforms and enterprise AI frameworks.
Establish architecture standards for security, identity, tenant isolation, data protection, observability, disaster recovery and operational resilience.
Develop reference architectures, high-level/low-level designs, capacity models, bills of materials, technology evaluations and implementation roadmaps.
Lead technical evaluations, proof-of-concepts, vendor assessments and architecture review boards.
Collaborate with infrastructure, network, security, storage, cloud, data, application and operations teams.
Define performance, availability, scalability, security and cost objectives and validate architecture against measurable acceptance criteria.
Provide technical leadership during deployment, migration, integration, troubleshooting and production transition.
Produce AI Factory reference architectures, solution blueprints, HLDs, LLDs, architecture diagrams, capacity/performance/scalability models, technology evaluations, security architecture inputs, BOMs, sizing, migration strategies, implementation roadmaps, operational readiness checklists, runbooks and acceptance criteria.

Requirements

What you’ll need

10+ years of infrastructure, cloud, enterprise architecture or solution architecture experience, with significant AI/GPU infrastructure exposure.
Proven experience designing large-scale enterprise platforms and translating business requirements into technical architectures.
Hands-on understanding of physical infrastructure, GPU systems, networking, storage and Linux platforms.
Bachelor's degree in Computer Science, Engineering, Information Technology or related field preferred.
NVIDIA certifications or equivalent GPU/AI infrastructure credentials preferred.
AWS Solutions Architect / Azure Solutions Architect certification preferred.
TOGAF or equivalent enterprise architecture certification preferred.
CCNP/CCIE or equivalent networking certification preferred.
CISSP or equivalent security certification preferred.
Kubernetes certifications such as CKA/CKAD preferred.
Red Hat / Linux certifications preferred.
AI/ML architecture expertise, including NVIDIA AI Enterprise, NGC, CUDA, NCCL, DCGM, GPU Operator and AI platform ecosystems.
PyTorch, TensorFlow and JAX, with operational understanding of training and inference workloads.
GPU scheduling, multi-tenancy, MIG/vGPU, GPU utilization and workload placement.
LLM, generative AI, RAG, fine-tuning, model serving and inference architecture.
NVIDIA A100/H100/H200/B200 or equivalent GPU platforms; NVLink, NVSwitch, PCIe topology and multi-GPU performance architecture.
DGX/HGX/OEM GPU server architecture and lifecycle management.
AI Factory capacity planning, rack density, power, cooling, commissioning and lifecycle strategy.
100/200/400/800G Ethernet, InfiniBand, RoCEv2, RDMA, Netris, BGP, EVPN/VXLAN, VRF, ECMP, VLAN, MTU, PFC, ECN and QoS.
NVIDIA ConnectX/SuperNIC, Spectrum/Spectrum-X, Quantum and BlueField DPU technologies.
GPU east-west traffic, GPUDirect RDMA and network performance troubleshooting.
Parallel file systems, object storage, NFS, NVMe/NVMe-oF and high-throughput data pipelines.
Ceph, WEKA, VAST, Dell PowerScale, Pure FlashBlade, NetApp or equivalent technologies.
Data lake/lakehouse concepts, metadata, lineage, data movement and data lifecycle.
GPUDirect Storage and storage/network performance optimization.
Kubernetes, GPU Operator, container runtimes, Kubernetes GPU scheduling and HPC or equivalent workload schedulers.
Model serving/inference platforms, MLOps platform architecture, API gateways, service discovery, secrets management and platform integration.
AWS and/or Azure AI infrastructure and security services.
Hybrid cloud connectivity, IAM, private networking, cloud storage, workload placement, cloud cost optimization, capacity planning and FinOps.
Zero Trust, network segmentation, IAM/RBAC, PAM, workload identity, GPU/DPU/container/Kubernetes/firmware/supply-chain security.
Encryption at rest/in transit, secrets management, audit logging, compliance controls, data/model protection, tenant isolation and secure model access.
Prometheus, Grafana, OpenTelemetry, NVIDIA DCGM and infrastructure telemetry.
Monitoring across GPU, CPU, memory, network, storage, power and thermal domains.
High availability, backup/restore, disaster recovery, business continuity, failure-domain design, performance engineering, bottleneck analysis, SLO/SLA design and capacity forecasting.