Staff Consulting Architect - AI Infrastructure

Nutanix is seeking a Staff Consulting Architect – AI to provide senior technical leadership in the architecture, design, and transformation of enterprise AI platforms based on the Nutanix AI Platform (NAI) and equivalent enterprise AI/ML platforms.

The role is intended for a highly experienced AI platform architect who can lead complex enterprise AI transformation initiatives spanning AI infrastructure, GPU-enabled Kubernetes, MLOps/LLMOps, Generative AI, RAG, model serving, data services, automation, security, and hybrid/multi-cloud environments.

The Sr. Staff Consulting Architect will act as a trusted technical advisor to senior customer stakeholders, helping organizations define their AI platform strategy, target architecture, adoption roadmap, and operating model.

The role requires a combination of deep hands-on expertise and strategic architecture leadership across AI/ML platforms, Kubernetes, virtualization, GPU infrastructure, cloud-native technologies, data platforms, and automation.

1. Enterprise AI Strategy & Architecture

• Lead architecture strategy for enterprise AI/ML and Generative AI platform transformation programs.

• Define target-state AI platform architectures aligned to customer business, data, application, security, and operational requirements.

• Architect enterprise AI platforms across:

• Nutanix AI Platform (NAI)

• Kubernetes-based AI platforms

• Nutanix AHV

• Bare-metal GPU infrastructure

• Hybrid and multi-cloud environments

• Public cloud AI/ML platforms where applicable

• Develop phased AI adoption roadmaps covering:

• AI infrastructure

• Model development

• Model training and fine-tuning

• Inference

• RAG

• MLOps/LLMOps

• Application integration

• Governance and operations

• Advise customer executives, enterprise architects, data scientists, ML engineers, platform teams, and application teams on enterprise AI adoption.

• Translate business use cases into scalable, secure, resilient, and operationally sustainable AI architectures.

• Evaluate architecture trade-offs across performance, scalability, GPU utilization, cost, security, data locality, and operational complexity.

2. AI Infrastructure & GPU Platform Architecture

• Design enterprise AI infrastructure supporting:

• GPU-enabled Kubernetes

• AI/ML workloads

• Model training

• Fine-tuning

• Inference

• Vector search

• RAG workloads

• Define GPU architecture considering:

• GPU selection and sizing

• GPU scheduling

• GPU partitioning and sharing

• CPU/GPU topology

• NUMA

• Memory requirements

• Network bandwidth

• Storage throughput

• Architect high-performance AI infrastructure across virtualized and bare-metal environments.

• Design GPU-enabled Kubernetes platforms using technologies such as:

• NVIDIA GPU Operator

• NVIDIA CUDA

• NVIDIA Container Toolkit

• NVIDIA Triton Inference Server

• KServe

• Evaluate and architect high-performance networking for AI workloads, including where applicable:

• RDMA

• InfiniBand

• RoCE

• High-speed Ethernet

• NVIDIA networking technologies

• Optimize infrastructure for GPU utilization, workload density, performance, availability, and cost.

• Define architectures for scalable AI compute pools and shared enterprise AI platforms.

3. Kubernetes & AI Platform Engineering

• Architect Kubernetes platforms optimized for enterprise AI workloads.

• Define AI platform architecture across:

• Kubernetes control plane

• GPU worker nodes

• Storage

• Networking

• Ingress

• Security

• Observability

• Design multi-cluster and multi-tenant AI environments.

• Define workload isolation across:

• Cluster

• Namespace

• Node

• GPU

• Architect integration with enterprise infrastructure and services including:

• Identity and access management

• LDAP/Active Directory

• DNS

• PKI/certificates

• Load balancers

• Storage

• Backup and DR

• Monitoring and logging

• Drive platform engineering and self-service capabilities for data scientists and AI developers.

• Define standardized patterns for AI workspace provisioning and lifecycle management.

4. Generative AI, RAG & AI Application Architecture

• Lead architecture for enterprise Generative AI solutions and platforms.

• Design end-to-end RAG architectures covering:

• Data ingestion

• Document processing

• Chunking

• Embedding generation

• Vector databases

• Retrieval

• Re-ranking

• Prompt orchestration

• LLM inference

• Architect enterprise AI application patterns using technologies such as:

• Large Language Models (LLMs)

• Embedding models

• Vector databases

• LangChain

• LlamaIndex

• NVIDIA NeMo

• NVIDIA NIM

• Triton

• KServe

• Define architectures for:

• LLM inference

• Model serving

• Fine-tuning

• RAG

• AI agents

• Enterprise copilots

• Evaluate open-source and commercial AI models and frameworks based on customer requirements.

• Design model deployment strategies considering:

• Performance

• Latency

• Throughput

• GPU utilization

• Model size

• Security

• Data privacy

• Cost

• Guide customers in moving AI prototypes and proof-of-concepts into production-grade platforms.

5. MLOps, LLMOps & Automation

• Define enterprise MLOps and LLMOps architectures covering the complete AI lifecycle.

• Design workflows for:

• Data preparation

• Model training

• Fine-tuning

• Model evaluation

• Model registration

• Model deployment

• Model monitoring

• Model retirement

• Integrate AI platforms with CI/CD and GitOps practices.

• Design automated AI platform provisioning using:

• Terraform

• Ansible

• Kubernetes operators

• APIs

• Establish automation for:

• GPU infrastructure provisioning

• Kubernetes clusters

• AI workspaces

• Model deployment

• Inference endpoints

• RAG pipelines

• Integrate automation with:

• Nutanix APIs

• NKP APIs

• Kubernetes APIs

• NVIDIA technologies

• Cloud provider APIs

• Develop repeatable, version-controlled, auditable, and supportable AI platform delivery patterns.

6. Data, Storage & AI Platform Integration

• Architect data platforms supporting enterprise AI workloads.

• Define integration between AI platforms and:

• File services

• Object storage

• Databases

• Data lakes

• Vector databases

• Enterprise data sources

• Design storage architectures optimized for:

• Model datasets

• Training data

• Model artifacts

• Checkpoints

• Embeddings

• Vector data

• Inference workloads

• Address performance requirements across:

• IOPS

• Throughput

• Latency

• Metadata performance

• Network bandwidth

• Define data movement and data locality strategies for hybrid AI environments.

• Establish architecture patterns for data protection, backup, disaster recovery, and lifecycle management.

7. AI Security, Governance & Responsible AI

• Define security architecture across the AI infrastructure, platform, data, model, and application layers.

• Establish controls for:

• Identity and access management

• RBAC

• Secrets management

• Network security

• Data protection

• Model access

• API security

• Design secure multi-tenant AI platforms with appropriate isolation.

• Define governance for:

• Models

• Datasets

• Prompts

• Embeddings

• AI applications

• Inference endpoints

• Establish security and governance patterns for enterprise RAG and GenAI applications.

• Address risks associated with:

• Sensitive data

• Data leakage

• Prompt injection

• Model misuse

• Unauthorized model access

• Integrate policy-as-code and security controls into AI platform lifecycle management.

• Guide customers toward responsible, auditable, and governed enterprise AI adoption.

8. Observability, Performance & Cost Optimization

• Define comprehensive observability for AI platforms covering:

• Infrastructure

• GPUs

• Kubernetes

• Models

• Inference services

• Applications

• Establish monitoring and optimization for:

• GPU utilization

• GPU memory

• CPU utilization

• Network performance

• Storage throughput

• Model latency

• Inference throughput

• Define AI platform SLOs, SLIs, and operational KPIs.

• Troubleshoot complex issues across:

• GPU infrastructure

• Kubernetes

• Networking

• Storage

• AI frameworks

• Model serving

• Application layers

• Drive capacity planning and AI infrastructure optimization.

• Develop strategies for optimizing AI infrastructure and cloud consumption costs.

9. Consulting, Technical Leadership & Customer Enablement

• Lead executive-level architecture workshops for:

• Enterprise AI strategy

• AI platform adoption

• Generative AI

• RAG

• MLOps/LLMOps

• AI infrastructure modernization

• Act as the senior technical advisor to customer:

• CIO/CTO organizations

• Enterprise architects

• Data and AI leaders

• Infrastructure leaders

• Platform engineering teams

• Data science and ML engineering teams

• Lead complex technical discovery and architecture engagements.

• Provide technical direction for strategic AI programs and high-value customer engagements.

• Act as the senior escalation point for complex AI architecture and implementation challenges.

• Review and govern AI architectures developed by Staff Consulting Architects and Consultants.

• Mentor architects and consultants and build organizational AI capability.

10. Reference Architecture, Innovation & Thought Leadership

• Create enterprise-grade AI reference architectures and solution blueprints.

• Develop reusable:

• Design patterns

• Implementation guides

• Automation frameworks

• AI solution templates

• MLOps/LLMOps patterns

• RAG reference implementations

• Identify and evaluate emerging AI technologies and their applicability to Nutanix solutions.

• Drive innovation across:

• Generative AI

• AI agents

• RAG

• GPU infrastructure

• AI platform engineering

• Model serving

• AI observability

• Collaborate with Product Management, Engineering, Support, and other technical teams to provide field feedback and influence product direction.

• Contribute to technical whitepapers, solution briefs, blogs, reference architectures, and internal enablement content.

• Represent Nutanix in strategic customer discussions, technical forums, and industry events where appropriate.

Technical Skills & Experience

Mandatory Skills – Must Have

• 10+ years of experience across infrastructure, cloud, virtualization, Kubernetes, AI/ML platforms, or related technologies.

• Proven experience architecting and delivering enterprise AI/ML platforms and AI infrastructure.

• Deep understanding of AI/ML platform architecture and enterprise AI adoption.

• Strong hands-on experience with:

• Kubernetes

• Nutanix AHV or equivalent virtualization platforms

• GPU-enabled infrastructure

• Linux

• Container technologies

• Strong experience with Nutanix AI Platform (NAI) or equivalent enterprise AI platforms.

• Deep understanding of GPU architecture and AI workload requirements.

• Strong understanding of:

• GPU scheduling

• CUDA

• GPU Operator

• GPU resource management

• GPU performance optimization

• Strong experience with Kubernetes-based AI platforms.

• Strong understanding of AI/ML frameworks and platforms, including several of:

• NVIDIA NeMo

• NVIDIA NIM

• Triton

• KServe

• Kubeflow

• MLflow

• Strong understanding of Generative AI and LLM architectures.

• Proven experience designing RAG architectures and AI application platforms.

• Strong understanding of:

• LLMs

• Embedding models

• Vector databases

• Model serving

• Fine-tuning

• Inference

• Strong experience with Infrastructure as Code:

• Terraform

• Ansible

• Strong experience with CI/CD, GitOps, and platform automation.

• Strong understanding of enterprise storage, networking, security, and data architecture for AI workloads.

• Strong troubleshooting skills across infrastructure, Kubernetes, GPU, networking, storage, and AI platform layers.

• Proven ability to lead complex customer engagements and architecture decisions.

• Excellent customer-facing, consulting, presentation, and executive communication skills.

Added Advantage – Good to Have

• Experience with NVIDIA AI Enterprise.

• Experience with NVIDIA DGX, HGX, or equivalent GPU infrastructure.

• Experience with NVIDIA networking technologies, InfiniBand, or RoCE.

• Experience with GPU partitioning and sharing technologies.

• Experience with Kubeflow, MLflow, KServe, Ray, or equivalent AI/ML platforms.

• Experience with LangChain, LlamaIndex, Flowise, or equivalent GenAI orchestration frameworks.

• Experience with vector databases such as Milvus, Qdrant, Weaviate, pgvector, or equivalent.

• Experience with AI agents and agentic architectures.

• Experience with multimodal AI.

• Experience with AI/ML model fine-tuning and optimization.

• Experience with public cloud AI services and GPU platforms on AWS or Azure.

• Experience with hybrid and multi-cloud AI architectures.

• Experience with AI governance, security, and responsible AI practices.

• Experience with FinOps and AI infrastructure cost optimization.

Trainable / Developable Skills

• Advanced AI agent and agentic workflow architectures.

• Emerging LLM and multimodal model architectures.

• Advanced model optimization and inference technologies.

• Emerging GPU and AI accelerator technologies.

• Advanced AI governance and responsible AI frameworks.

• Advanced AI observability and LLM evaluation frameworks.

• New Nutanix AI platform capabilities and ecosystem integrations.

Education & Certifications

Education

• Bachelor's Degree in Engineering, Computer Science, Artificial Intelligence, Data Science, or equivalent discipline.

• Master's degree in Computer Science, AI/ML, Data Science, or a related discipline is an advantage.

Preferred Certifications

• Nutanix certifications:

• NCP

• NAI / relevant Nutanix AI certification

• NCX preferred

• Certified Kubernetes Administrator (CKA)

• Certified Kubernetes Application Developer (CKAD)

• NVIDIA certifications relevant to AI/ML or data center technologies

• Terraform Associate

• Relevant AWS/Azure certifications

• Relevant AI/ML or cloud architecture certifications

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Location & Work Shifts

• Pune / Bangalore – Hybrid model (3 days a week)

• Travel as required for customer assignments

• Willingness to work US and EMEA shifts based on customer and engagement requirements

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