Principal AI Platform Architect Enterprise GenAI

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.

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.

• 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.
• Strong 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.

• 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.