Designs and architect end-to-end AI Cloud platforms with a focus on security, cost-efficiency, and performance. This position involves direct client engagement to translate requirements into technical Solution, encompassing GPU infrastructure rightsizing and optimal model selection. We are looking for a cloud expert with a demonstrated ability to transition complex AI models from concept to large-scale production.

Company: Larsen & Toubro

Role: AI Architect

Location: Chennai

Experience:

15 - 20 years of IT Experience
minimum 5 years in AI platform

Key Skills:

Artificial Intelligence
Python
Kubernetes
Infrastructure Management
NVIDIA GPU Architecture
InfiniBand
Intel
Google GPU Architecture
Slurm
OpenShift
PyTorch
TensorFlow
LangChain
LangGraph
Transformers
Attention mechanisms
Diffusion
MoE
RLHF
Pinecone
FAISS
Chroma
OpenAI
VLLM
RAG
agentic AI workflows
high-performance storage (Lustre, PFS, Object NVMe)
NVIDIA architectures (Hopper, Blackwell)

Qualification:

Bachelor of Technology (BTech)
BE/B-Tech or equivalent with Computer Science or Electronics & Communication

Role Focus:

Translate business requirements into scalable, high-performance AI/GenAI architectures featuring NVIDIA GPU clusters
Design end-to-end AI Cloud and next-generation platforms optimized for deep learning workloads and distributed training
Architect HPC cluster topologies utilizing high-speed InfiniBand (NDR/HDR) and RoCE v2 interconnects for low-latency communication
Right-size platform components, including GPUs, CPUs, memory and NVMe storage for comprehensive client proposals
Architect distributed training and inference environments optimized for MPI frameworks and workload scheduling via Slurm
Design scalable container orchestration platforms using Kubernetes and Kubeflow to manage AI workloads
Propose optimized inference strategies using vLLM, Triton, and TensorRT-LLM to meet specific latency and throughput KPIs
Develop private AI cloud environments focused on data sovereignty and regulatory compliance, such as the India DPDP Act
Define integration strategies for LLMs and open-source models within existing enterprise data systems, APIs, and knowledge graphs
Establish reference architectures for CI/CD/CT pipelines and automated model retraining workflows to ensure reproducibility
Implement automation and observability frameworks for monitoring GPU utilization, performance tuning, and failure handling
Drive technical validation through Proof of Concept (PoC) engagements, focusing on scalability and performance benchmarks for LLM training
Establish Infrastructure-as-Code (IaC) practices to ensure reproducible and reliable cluster deployments

Nice to have:

Currently in AI / Cloud Presales team
Hands-on with Python, vector DBs (Pinecone, FAISS, Chroma), and LLM APIs (OpenAI, Anthropic)
Solid understanding of cloud-native architecture OpenStack, KVM, (Azure/AWS/GCP), microservices, Kubernetes, serverless, API gateways
Good knowledge on deep learning experience: CNNs, RNNs/LSTMs, Transformers, and attention mechanisms
Proficiency in Python for ML: NumPy, pandas, scikit-learn, and frameworks such as PyTorch or TensorFlow
Experience in integrating LLMs (GPT, Claude, Gemini, LLaMA, Mistral) into applications
Prompt engineering skills: zero-shot, few-shot, chain-of-thought, ReAct, and structured output patterns
Experience building RAG systems: document chunking, embedding models, vector search, and retrieval optimization
Understanding of AI agent patterns, tool use, and agentic workflows
Familiarity with Docker, CI/CD pipelines, and Git-based workflows

Other:

Strong problem-solving and analytical thinking
Excellent communication and stakeholder management
Ability to influence leadership and drive strategic decisions
Innovation mindset with focus on enterprise impact

AI Architect

Skills Required

Description