About the Role

Office Beacon is looking for a Principal AI Platform Architect – Enterprise GenAI to define the technical vision, architecture, and engineering strategy for an enterprise-grade AI platform.

The platform will support secure and scalable deployment of Small Language Models (SLMs), Large Language Models (LLMs), Vision Language Models (VLMs), Retrieval-Augmented Generation (RAG), and AI agent workflows within enterprise environments.

The Principal AI Platform Architect will own end-to-end architectural direction, from technology selection and platform design through implementation, deployment, optimization, governance, and continuous evolution.

This is a hands-on architecture role requiring deep experience building and operating production AI/ML platforms, GPU infrastructure, model-serving systems, distributed systems, and cloud-native infrastructure.

Key Responsibilities

AI Platform Architecture

- Define the overall architecture and technical vision for the Enterprise GenAI platform.
- Establish architectural standards for AI applications, model serving, data flows, infrastructure, and platform services.
- Evaluate technologies and make architecture decisions based on scalability, reliability, security, performance, and maintainability.
- Define technical roadmaps for the continued evolution of the AI platform.

Model Serving & GPU Infrastructure

- Lead architecture decisions related to GPU infrastructure, model serving, and model lifecycle management.
- Design production-grade training and inference environments for AI workloads.
- Architect scalable model-serving infrastructure using technologies such as vLLM, Hugging Face TGI, TensorRT-LLM, or similar platforms.
- Optimize GPU utilization, inference performance, and infrastructure efficiency.

GenAI, RAG & AI Agents

- Architect production-grade RAG systems involving embeddings, semantic search, retrieval, reranking, and vector databases.
- Design AI agent architectures and production workflows.
- Define architectures supporting LLMs, SLMs, VLMs,



and other generative AI workloads.
- Establish approaches for model evaluation, deployment, monitoring, and lifecycle management.

Model Adaptation & MLOps

- Define approaches for model fine-tuning and adaptation using techniques such as LoRA, QLoRA, and PEFT.
- Establish model development, evaluation, deployment, and monitoring workflows.
- Implement or guide CI/CD processes for AI models, applications, and infrastructure.
- Use appropriate MLOps technologies and practices to improve reproducibility and operational reliability.

Cloud & Infrastructure

- Design and operate cloud-native AI infrastructure on AWS, Microsoft Azure, GCP, or comparable platforms.
- Architect Kubernetes-based AI workloads and containerized services.
- Define infrastructure-as-code practices using Terraform or similar technologies.
- Design distributed systems capable of supporting enterprise AI workloads at scale.

Security & Enterprise Architecture

- Define security controls for AI workloads and platform infrastructure.
- Architect appropriate tenant isolation and data-separation strategies.
- Collaborate with Security and DevOps teams on infrastructure and application security.
- Contribute to enterprise AI governance and operational standards.

Technical Leadership

- Provide technical direction to AI engineers and platform engineering teams.
- Review architectural proposals and technical designs.
- Establish engineering standards and best practices across AI platform initiatives.
- Collaborate with DevOps, Product, QA, Security, and other engineering teams.
- Mentor engineers and help resolve complex architectural and technical challenges.

3. Must-Have Qualifications




- 10+ years of professional software engineering experience.
- 5+ years of hands-on experience building and operating production AI/ML or Generative AI platforms.
- Demonstrated experience designing and deploying enterprise-grade AI systems.
- Strong production experience with multiple enterprise GenAI technologies, including LLMs, SLMs, VLMs, RAG, and AI agents.
- Robust proficiency in Python and PyTorch.
- Hands-on experience with Hugging Face Transformers.
- Production experience developing AI services and APIs using FastAPI or similar frameworks.
- Strong understanding of LoRA, QLoRA, PEFT, and model fine-tuning.
- Strong understanding of embeddings, semantic search, and vector databases.
- Hands-on experience with production model-serving or MLOps technologies such as vLLM, Hugging Face TGI, TensorRT-LLM, Ray, or MLflow.
- Strong experience with Kubernetes and Docker.
- Strong experience with Terraform or another infrastructure-as-code technology.
- Strong experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
- Experience designing or operating GPU-based cloud infrastructure.
- Strong knowledge of software architecture and distributed systems.
- Proven ability to make architecture decisions for complex production systems.

Preferred Qualifications
- Experience designing AI platforms for multi-tenant enterprise environments.
- Experience implementing tenant isolation and data-separation strategies.
- Experience with SOC 2 or ISO 27001 requirements in technology environments.
- Experience with document intelligence and OCR-based AI workflows.
- Experience with GPU optimization and inference performance tuning.
- Experience with model quantization techniques.
- Experience operating large-scale Kubernetes environments.
- Experience establishing AI platform governance, observability, and reliability practices.
- Experience leading technical architecture across multiple engineering teams.

📌 Principal AI Platform Architect – Enterprise GenAI (Vadodara)
🏢 Office Beacon ASPL
📍 Vadodara