Staff Consulting Architect - AI Infrastructure

Nutanix, Inc.; Pune District; Hybrid; INR 4,500,000 - 6,500,000; Full time

Job summary

Nutanix, Inc. in Pune/Bangalore is seeking a Staff Consulting Architect for AI Infrastructure to provide senior technical leadership in architecting enterprise AI platforms based on the Nutanix AI Platform (NAI) and related technologies.

You will shape target-state AI architectures, drive multi-cloud adoption, and oversee GPU-enabled infrastructure, MLOps/LLMOps, and governance across hybrid environments. The role requires deep hands-on expertise and strategic leadership, advising executives and

Job description

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.

Nutanix AI Platform (NAI)

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 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.

Inference

Vector search

GPU selection and sizing

GPU partitioning and sharing

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.

Architect Kubernetes platforms optimized for enterprise AI workloads.

Storage

Networking

Ingress

Security

Observability

Design multi-cluster and multi-tenant AI environments.

Define workload isolation across:

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.

Lead architecture for enterprise Generative AI solutions and platforms.

Chunking

Retrieval

Re-ranking

LLM inference

Architect enterprise AI application patterns using technologies such as:

LangChain

NVIDIA NeMo

NVIDIA NIM

Triton

KServe

LLM inference

RAG

AI agents

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

Performance

Throughput

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.

Data preparation

Integrate AI platforms with CI/CD and GitOps practices.

Terraform

Ansible

APIs

Inference endpoints

Integrate automation with:

Nutanix APIs

NKP APIs

NVIDIA technologies

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

Design storage architectures optimized for:

Checkpoints

Embeddings

Inference workloads

Address performance requirements across:

IOPS

Throughput

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.

Identity and access management

RBAC

Secrets management

Network security

Data protection

API security

Design secure multi-tenant AI platforms with appropriate isolation.

Define governance for:

Datasets

Embeddings

AI applications

Inference endpoints

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

Address risks associated with:

Data leakage

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

Inference services

Applications

Establish monitoring and optimization for:

GPU memory

Network performance

Storage throughput

Inference throughput

Define AI platform SLOs, SLIs, and operational KPIs.

Troubleshoot complex issues across:

GPU infrastructure

Networking

Storage

AI frameworks

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

Generative AI

RAG

MLOps/LLMOps

AI infrastructure modernization

Act as the senior technical advisor to customer:

CIO/CTO organizations

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.

Design patterns

Automation frameworks

AI solution templates

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 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:

Nutanix AHV or equivalent virtualization platforms

GPU-enabled infrastructure

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:

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

MLflow

Strong understanding of Generative AI and LLM architectures.

Proven experience designing RAG architectures and AI application platforms.

Strong understanding of:

LLMs

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

NCX preferred

NVIDIA certifications relevant to AI/ML or data center technologies

Terraform Associate

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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Nutanix is an equal opportunity employer.

Nutanix is an Equal Employment Opportunity and (in the U.S.) an affirmative Action employer. Qualified applicants are considered for employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, marital status, protected veteran status, disability status or any other category protected by applicable law. We hire and promote individuals solely on the basis of qualifications for the job to be filled. We strive to foster an inclusive working environment that enables all our Nutants to be themselves and to do great work in a safe and welcoming environment, free of unlawful discrimination, intimidation or harassment. As part of this commitment, we will ensure that persons with disabilities are provided reasonable accommodations. If you need a reasonable accommodation, please let us know by contacting [e-mail] .