I
AI Systems Architect
Not specifiedFull-timeAI & Machine LearningEnterprise SoftwareData Infrastructure
Lead the architecture of internal AI infrastructure and engineering platforms for an enterprise storage technology company. Design production-grade, on-premises AI systems spanning LLM inference, RAG, developer tooling, and secure platform integrations.
Responsibilities
- Define the AI platform roadmap across hardware, software, and AIOps.
- Design AI-focused enterprise products and infrastructure for performance, cost, and power efficiency.
- Architect on-premises, air-gapped LLM inference environments with GPU-based multi-model serving and network isolation.
- Design and extend RAG pipelines for document and source-code collections, including parsing, chunking, embeddings, governance, and vector storage.
- Build the internal AI platform, including chatbot access, model routing, MCP and skills ecosystems, and secure on-premises/cloud data boundaries.
- Develop internal developer tools and agentic coding workflows using local model endpoints and coding assistants.
- Evaluate hardware and model reliability for server and storage platforms using quantitative analysis.
- Establish secure, reproducible, maintainable operational practices for AI systems.
Requirements
- End-to-end systems architecture experience with strong understanding of technical trade-offs.
- Hands-on experience with LLMs, AI agents, MCPs, and production AI system deployment.
- Expertise in Linux systems engineering.
- Experience with enterprise storage and server hardware.
- Proficiency in Python, data pipelines, internal tooling, and service integrations.
- Working knowledge of ETL, data warehouses, business intelligence, and analytics tooling.
- Ability to lead and collaborate with Product, R&D, Operations, and systems validation teams.
- Strong quantitative analysis, communication, and technical presentation skills.