AI Architect

About Deliverance AI

Deliverance AI is the production AI platform company. We exist because 94% of enterprises fail to scale AI — not for lack of ambition or budget, but for lack of platform, governance, and delivery capability. We close the gap between AI investment and AI production for enterprises across regulated industries including pharma and biotech, financial services, retail, telecommunications, and logistics.

Our proprietary nine-layer platform — built around three core capabilities: Clarity (see everything), Govern (control everything), and Accelerate (ship everything) — is deployed inside customer environments with live workloads running on it from day one. We are not a consultancy that writes strategy decks. We are not a staffing firm that lends contractors. We are an engineering-led company with proprietary platform IP, a growing agent marketplace, and 15+ pre-built AI blueprints that cut months off delivery timelines.

Our engagement model is simple: Assess (4 weeks), Deploy (12–16 weeks), Operate (ongoing). Dedicated engineering pods own delivery end-to-end. Every deployment compounds the platform. Every use case ships faster than the last. Governed, observable, and delivering value from day one.

About the role

As AI Architect, you will be the technical authority on how our nine-layer platform is configured and deployed across enterprise customer environments. This is the role that designs the architecture blueprint during the Assessment phase, selects models, configures agents from our marketplace, defines governance policies, and ensures the platform layers are tailored to each customer's specific regulatory, infrastructure, and use-case requirements.

You will work directly with customer CTOs, VPs of Engineering, and Heads of AI during discovery and design — translating business ambition into robust, governed, production-grade architectures. You will determine which of the nine platform layers are critical for each engagement (typically 4–6 in a first deployment), design inference plane topologies, configure the AI Gateway routing and policy enforcement, and define how Clarity, Govern, and Accelerate capabilities map to the customer's environment.

You will also shape the evolution of the platform itself — feeding learnings from customer deployments back into our reference architectures, blueprints, and agent marketplace. Every engagement you lead makes the next one faster.

What you will do

• Lead architecture design for enterprise AI deployments, producing Solution Design Documents, Architecture Blueprints, and integration patterns tailored to each customer's environment and regulatory context.
• Design configurations of the nine-layer platform for each engagement — AI Gateway, Five Registries, Agent Governance, Inference Platform, GPU Infrastructure, Data & RAG, Observability & FinOps, Governance & Compliance, and Developer Experience.
• Select and configure agents from the Deliverance AI marketplace for customer-specific use cases, including RAG pipelines, agentic workflows, document processing, fraud detection, and compliance automation.
• Define governance architectures that satisfy EU AI Act, NIST AI RMF, ISO 42001, and GxP requirements — deployed as code via the ARMOR security framework, not as documents.
• Evaluate and recommend model selection strategies across open-source families based on customer use cases, licensing, performance, and cost trade-offs.
• Work within engineering pods alongside ML Engineers and Data Engineers during the Deploy phase, providing architectural oversight through the four gated phases.
• Collaborate with strategic partners on joint solution architecture for complex, multi-vendor customer environments.
• Contribute to the blueprint library — documenting reusable patterns that accelerate future engagements and compound the platform's value.

What we are looking for

• 8+ years of experience in software or infrastructure architecture, with at least 3 years focused on AI/ML systems in production environments.
• Deep hands-on knowledge of inference infrastructure, model serving frameworks (vLLM, TensorRT-LLM, Triton), and GPU compute orchestration.
• Strong understanding of Kubernetes, cloud-native architecture, and hybrid/on-premise deployment models across major cloud providers.
• Experience designing systems for regulated industries with stringent compliance, auditability, and data residency requirements — pharma (GxP), financial services (FCA), and government sectors.
• Familiarity with the open-source model landscape, fine-tuning pipelines, RAG architectures, agentic AI patterns, and model evaluation methodologies.
• Excellent communication skills — you can whiteboard a complex architecture for a CTO and then write the detailed specification for an engineering team to build against.
• Experience in consulting, professional services, or vendor-side architecture roles with direct enterprise customer engagement.
• An engineering-led mindset that values production reliability, governed deployments, and compounding value over theoretical elegance.