Cloud Architect - Product Infrastructure
First seen by Alion on Sep 3, 2026.
Job Description :
- 5 - 7 years of hands-on cloud architecture experience across at least two of AWS, GCP, and Azure - not certifications alone, but systems you have actually designed and operated under load.
- 2 - 3 years working directly with GenAI technologies - LLMs, vector stores, embedding pipelines, RAG architectures, or agentic frameworks in production.
- Deep familiarity with Node.js / npm runtime architectures and how they behave in containerized, cloud-native deployment environments.
- Experience designing multi-tenant SaaS infrastructure with enterprise security requirements - auth, audit, access control, isolation boundaries.
- Proven ability to architect deployment pipelines for codebases, APIs, and AI inference services - not just CI/CD for web apps.
- Familiarity with vector databases (Qdrant, Pinecone, or equivalent) and the infrastructure patterns around embedding generation and semantic retrieval at scale.
- Comfort with Python-based backends (FastAPI or equivalent) - you do not need to write all the code, but you need to own the infrastructure it runs on.
- Experience with infrastructure-as-code (Terraform or equivalent) - Terraform is already in the Synapse stack.
Key Responsibilities :
- Architect and maintain highly available, scalable cloud infrastructure across GCP, AWS, and Azure to support intensive AI model inference and data processing.
- Automate infrastructure provisioning and lifecycle management using Terraform to ensure consistency and speed across development and production environments.
- Design and implement high-performance vector database solutions using Qdrant to enable low-latency retrieval for complex AI agent workflows.
- Integrate and optimize CrewAI frameworks and Anthropic model pipelines to enhance the efficiency and reliability of our autonomous agent systems.
- Develop and maintain internal APIs using FastAPI to facilitate seamless communication between our infrastructure services and external client applications.
- Establish and manage Model Context Protocol (MCP) standards to ensure interoperability and data integrity across our distributed AI ecosystem.
- Collaborate with cross-functional teams to troubleshoot performance bottlenecks and implement proactive monitoring strategies that improve overall system uptime.
Skills
Cloud Architect, AWS, Azure, Google Cloud Platform, RAG, Agentic AI, SaaS, CI/CD, VectorDB, Python, IAC Terraform