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.

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

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.

AI Infrastructure & GPU Platform Architecture

Design enterprise AI infrastructure supporting

GPU-enabled Kubernetes

AI/ML workloads

Model training

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.

Kubernetes & AI Platform Engineering

Architect Kubernetes platforms optimized for enterprise AI workloads.

Define AI platform architecture across

Kubernetes control plane

GPU worker nodes

Ingress

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.

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

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

Throughput

GPU utilization

Model size

Security

Data privacy

Cost

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

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

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.

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.

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

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.

Observability, Performance & Cost Optimization

Define comprehensive observability for AI platforms covering

Infrastructure

GPUs

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.

Consulting, Technical Leadership & Customer Enablement

Lead executive-level architecture workshops for

Enterprise AI strategy

AI platform adoption

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.

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

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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